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| Title: | 個人化注意力引導機制對於混合實境虛擬化學實驗室學習成效的影響研究
The Effect of Personalized Attention-Guiding Mechanisms on Learning performance in a Mixed Reality-based Virtual Chemistry Laboratory |
| Authors: | 潘昱穎
Pan, Yu-Ying |
| Contributors: | 陳志銘
Chen, Chih-Ming
潘昱穎
Pan, Yu-Ying |
| Keywords: | 虛實整合化學實驗
混合實境
注意力診斷與回饋
個人化注意力引導機制
學習成效
工作負荷
學習動機
學習滿意度
Virtual-Real Integrated Chemistry Experiment
Mixed Reality
Personalized Attention Guidance Mechanism
Attention Diagnosis and Feedback
Learning Performance
Workload
Learning Motivation
Learning Satisfaction |
| Date: | 2024 |
| Issue Date: | 2025-02-04 16:09:56 (UTC+8) |
| Abstract: | 混合實境所提供之融合虛擬與實境特性，非常適合將其應用於發展虛實整合之化學實驗室，來協助學習者在安全的環境下學習化學實驗之操作知識與技能，為科學教育的創新帶來了新的可能，然而學習者透過混合實境進行學習時，若遇到學習上的困難，其他人難以即時提供協助，容易使學習者感受到心理上的壓力。此外，基於混合實境所發展之虛實整合化學實驗室學習場景中往往包含許多競爭性資訊，可能干擾學習者的注意力分配，進一步影響學習者的學習成效。因此，本研究基於眼動序列與機器學習發展「具有個人化注意力引導機制」之「混合實境化學實驗學習系統」，來幫助學習者診斷學習過程中之不正確注意力投注行為，並進行正確之注意力引導，希望據此來提升學習者在使用「混合實境化學實驗學習系統」輔以進行化學實驗學習時的視覺注意力認知，以促進學習者的學習成效。
本研究採用準實驗研究法，以基隆市某高中兩個班級的學生合計 27 名為研究對象，並將其隨機分派為實驗組與控制組進行酸鹼滴定實驗。13名實驗組學習者使用「具有個人化注意力引導機制之混合實境化學實驗學習系統」，14名控制組學習者則使用「不具有個人化注意力引導機制之混合實境化學實驗學習系統」。本研究旨在探討採用有/無「個人化注意力引導機制」之「混合實境化學實驗學習系統」輔以進行酸鹼滴實驗，對於學習者的學習成效、工作負荷、學習動機、學習滿意度，以及學習行為是否具有顯著的差異。本研究也以學習者的先備知識與認知風格為背景變項，進一步探討其對於學習成效、工作負荷、學習動機，以及學習滿意度的影響。
研究結果顯示，在學習成效方面，組間比較上的兩組、高低先備知識以及不同認知風格學習者的學習成效皆不具有顯著的差異；組內比較上的實驗組學習者於實驗前後的學習成效具有顯著的提升，但控制組學習者的學習成效並無顯著的提升；組內比較上的低先備知識的兩組學習者於實驗前後的學習成效皆具有顯著的提升，但高先備知識的兩組學習者的學習成效並無顯著的提升；組內比較上的場地獨立型的實驗組學習者的學習成效具有顯著的提升，但場地獨立型的控制組學習者的學習成效則並無顯著的提升，而場地相依型的兩組學習者的學習成效則沒有顯著的差異。在工作負荷方面，低先備知識的控制組的生理需求顯著大於實驗組學習者。在學習動機方面，控制組與實驗組學習者的整體學習動機、價值面向、期望面向、情緒面向皆沒有顯著的差異、但前三者的平均得分均高於量表得分中位數，情緒面向上則低於量表得分中位數。在學習滿意度方面，控制組與實驗組學習者的學習滿意度不具有顯著的差異，但平均得分均高於量表得分中位數。此外，採用「具有個人化注意力引導機制之混合實境化學實驗學習系統」輔以進行酸鹼滴定實驗的實驗組學習者，部份學習段落的注意力提醒建議次數與學習成效進步幅度具有顯著的正相關。最後，研究亦透過兩組學習者與教學者的質性訪談資料分析採用有/無「個人化注意力引導機制之混合實境化學實驗學習系統」輔以進行酸鹼滴定實驗學習者的使用感受與建議，並結合研究結果歸納出教學改善建議與未來研究方向。
The characteristics of the fusion of virtual and real environments provided by mixed reality are very suitable for the development of chemical laboratories that integrate virtual and real environments to assist learners in learning the operational knowledge and skills of chemical experiments in a safe environment, thus creating innovations in science education. It brings new possibilities. However, when learners encounter learning difficulties when learning through mixed reality, it is difficult for others to provide immediate assistance, which can easily cause learners to feel psychological pressure. In addition, virtual-real integrated chemistry laboratory learning scenarios developed based on mixed reality often contain a lot of competing information, which may interfere with learners' attention allocation and further affect learners' learning performance. Therefore, this research develops a "Mixed Reality Chemistry Experiment Learning System with a Personalized Attention Guidance Mechanism" based on eye movement sequences and machine learning to help learners diagnose incorrect attention behaviors during the learning process. Through correct attention guidance, we hope to improve learners' visual attention cognition when using the "Mixed Reality Chemistry Experiment Learning System with a Personalized Attention Guidance Mechanism" to supplement chemical experiment learning, so as to promote learners' learning performance.
This research adopted a quasi-experimental research method, taking a total of 27 students from two classes of high school in Keelung City as the research subjects, and randomly assigning them into an experimental group and a control group to conduct an acid-base titration experiment. Thirteen learners in the experimental group used the "Mixed Reality Chemistry Experiment Learning System with a Personalized Attention Guidance Mechanism," while 14 learners in the control group used the "Mixed Reality Chemistry Experiment Learning System without a Personalized Attention Guidance Mechanism." This research aims to explore the effects of using a "Mixed Reality Chemistry Experiment Learning System with or without a Personalized Attention Guidance Mechanism" supplemented by acid-base titration experiments on learners' learning performance, workload, learning motivation, and learning satisfaction, and whether there is a significant difference in learning behavior. This research also uses learners' prior knowledge and cognitive style as background variables to further explore their impact on learning performance, workload, learning motivation, and learning satisfaction.
The research results show that in terms of learning performance, there is no significant difference in the learning performance of the two groups, high and low prior knowledge, and learners with different cognitive styles in the inter-group comparison; in the intra-group comparison, the learning performance of the experimental group learners before and after the experiment There is a significant improvement in performance, but there is no significant improvement in the learning performance of the control group of learners. The learning performance of the two groups of learners with low prior knowledge in the group has significantly improved before and after the experiment, but the learning performance of the two groups of learners with high prior knowledge has significantly improved. There is no significant improvement in the learning performance of learners in the site-independent experimental group. However, there is no significant improvement in the learning performance of learners in the site-independent control group. However, there is no significant improvement in the learning performance of learners in the site-dependent control group. There is no significant difference in the learning performance of the two groups of learners. In terms of workload, the physiological demands of the control group with low prior knowledge were significantly greater than those of the experimental group of learners, and there were no significant differences in the rest. In terms of learning motivation, there is no significant difference in the overall learning motivation, value aspect, expectation aspect, and emotional aspect of the learners in the control group and the experimental group. However, the average scores of the first three are all higher than the median score of the scale. The emotional aspect above is lower than the scale score median. In terms of learning satisfaction, there is no significant difference between the learning satisfaction of learners in the control group and the experimental group, but the average scores are higher than the median score of the scale.
Additionally, when the "Mixed Reality Chemistry Experiment Learning System with a Personalized Attention Guidance Mechanism" was used to supplement the acid-base titration experiment in the experimental group, the number of recommended attention reminders for certain learning passages showed a significant positive correlation with improvements in learning performance. Finally, the research also analyzed qualitative interview data from learners and teachers in both groups to understand their experiences and gather suggestions for teaching improvement and future research directions. |
| Reference: | 一、 中文文獻
吳裕益(1987)。認知能力與認知型態個別差異現象之探討。國立高雄師範學院教育學系及教育研究所教育期刊，7，51-98。
二、 英文文獻
Adriana Cárdenas-Robledo, L., Hernández-Uribe, Ó., Reta, C., & Antonio Cantoral-Ceballos, J. (2022). Extended reality applications in industry 4.0. – A systematic literature review. Telematics and Informatics, 73, 101863. https://doi.org/10.1016/j.tele.2022.101863
Aguayo, C., & Eames, C. (2023). Using mixed reality (XR) immersive learning to enhance environmental education. The Journal of Environmental Education, 54(1), 58–71. https://doi.org/10.1080/00958964.2022.2152410
Akizuki, K., & Ohashi, Y. (2015). Measurement of functional task difficulty during motor learning: What level of difficulty corresponds to the optimal challenge point? Human Movement Science, 43, 107–117. https://doi.org/10.1016/j.humov.2015.07.007
Aljowaysir, N., Ozdemir, T. O., & Kim, T. (2019). Differentiated Learning Patterns with Mixed Reality. 2019 IEEE Games, Entertainment, Media Conference (GEM), 1–4. https://doi.org/10.1109/GEM.2019.8811558
Alpizar, D., Adesope, O. O., & Wong, R. M. (2020). A meta-analysis of signaling principle in multimedia learning environments. Educational Technology Research and Development, 68(5), 2095–2119. https://doi.org/10.1007/s11423-020-09748-7
Arbel, Y., Feeley, E., & He, X. (2020). The Effect of Feedback on Attention Allocation in Category Learning: An Eye Tracking Study. Frontiers in Psychology, 11, 559334. https://doi.org/10.3389/fpsyg.2020.559334
Armougum, A., Orriols, E., Gaston-Bellegarde, A., Marle, C. J.-L., & Piolino, P. (2019). Virtual reality: A new method to investigate cognitive load during navigation. Journal of Environmental Psychology, 65, 101338. https://doi.org/10.1016/j.jenvp.2019.101338
Arroyo, D. M., Rodríguez, P. S., Calle, D. P., Kloos, C. D., Espiga, M. I., & Hernández-Leo, D. (2010). Assessment in 3D virtual worlds: QTI in Wonderland. In Congreso iberoamericano de informática educative (pp. 410-417).
Arslan‐Ari, I. (2018). Learning from instructional animations: H ow does prior knowledge mediate the effect of visual cues? Journal of Computer Assisted Learning, 34(2), 140–149. https://doi.org/10.1111/jcal.12222
Bell, B., Bradley, J. D., & Steenberg, E. (2015). Chemistry Education Through Microscale Experiments. In J. García‐Martínez & E. Serrano‐Torregrosa (Eds.), Chemistry Education (1st ed., pp. 539–562). Wiley. https://doi.org/10.1002/9783527679300.ch22
Betrancourt, M. (2005). The animation and interactivity principles in multimedia learning. The Cambridge handbook of multimedia learning, 287-296.
Bidarra, J., & Rusman, E. (2017). Towards a pedagogical model for science education: Bridging educational contexts through a blended learning approach. Open Learning: The Journal of Open, Distance and e-Learning, 32(1), 6–20. https://doi.org/10.1080/02680513.2016.1265442
Birchfield, D., & Megowan-Romanowicz, C. (2009). Earth science learning in SMALLab: A design experiment for mixed reality. International Journal of Computer-Supported Collaborative Learning, 4(4), 403–421. https://doi.org/10.1007/s11412-009-9074-8
Bittermann, A., McNamara, D., Simonsmeier, B. A., & Schneider, M. (2023). The Landscape of Research on Prior Knowledge and Learning: A Bibliometric Analysis. Educational Psychology Review, 35(2), 58. https://doi.org/10.1007/s10648-023-09775-9
Borji, A., & Itti, L. (2013). State-of-the-Art in Visual Attention Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 185–207. https://doi.org/10.1109/TPAMI.2012.89
Bourgeois, A., Guedj, C., Carrera, E., & Vuilleumier, P. (2020). Pulvino-cortical interaction: An integrative role in the control of attention. Neuroscience & Biobehavioral Reviews, 111, 104–113. https://doi.org/10.1016/j.neubiorev.2020.01.005
Cai, J.-Y., Wang, R.-F., Wang, C.-Y., Ye, X.-D., & Li, X.-Z. (2021). The Influence of Learners’ Cognitive Style and Testing Environment Supported by Virtual Reality on English-Speaking Learning Achievement. Sustainability, 13(21), 11751. https://doi.org/10.3390/su132111751
Carrasco, M. (2014). Spatial covert attention: Perceptual modulation. The Oxford handbook of attention,183, 230.
Chandra, M. A., & Bedi, S. S. (2021). Survey on SVM and their application in image classification. International Journal of Information Technology, 13(5), 1–11. https://doi.org/10.1007/s41870-017-0080-1
Chang, J.-J., Lin, W.-S., & Chen, H.-R. (2019). How attention level and cognitive style affect learning in a MOOC environment? Based on the perspective of brainwave analysis. Computers in Human Behavior, 100, 209–217. https://doi.org/10.1016/j.chb.2018.08.016
Chen, C.-M., Li, M.-C., & Tu, C.-C. (2024). A Mixed Reality-Based Chemistry Experiment Learning System to Facilitate Chemical Laboratory Safety Education. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10101-3
Chen, C.-M., & Wang, J.-Y. (2018). Effects of online synchronous instruction with an attention monitoring and alarm mechanism on sustained attention and learning performance. Interactive Learning Environments, 26(4), 427–443. https://doi.org/10.1080/10494820.2017.1341938
Chen, S. Y., & Chang, L.-P. (2016). The influences of cognitive styles on individual learning and collaborative learning. Innovations in Education and Teaching International, 53(4), 458–471. https://doi.org/10.1080/14703297.2014.931242
Chen, S. Y., Huang, P.-R., Shih, Y.-C., & Chang, L.-P. (2016). Investigation of multiple human factors in personalized learning. Interactive Learning Environments, 24(1), 119–141. https://doi.org/10.1080/10494820.2013.825809
Chen, C., Wang, J., & Yu, C. (2017). Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. British Journal of Educational Technology, 48(2), 348–369. https://doi.org/10.1111/bjet.12359
Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88–113. https://doi.org/10.1016/j.edurev.2013.01.001
Chiang, T. H. C., Yang, S. J. H., & Hwang, G.-J. (2014). Students’ online interactive patterns in augmented reality-based inquiry activities. Computers & Education, 78, 97–108. https://doi.org/10.1016/j.compedu.2014.05.006
Chica, A. B., Martín-Arévalo, E., Botta, F., & Lupiánez, J. (2014). The Spatial Orienting paradigm: How to design and interpret spatial attention experiments.Neuroscience & Biobehavioral Reviews,40, 35-51.
Clark, R. C., & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. john Wiley & sons.
Commodari, E. (2012). Attention Skills and Risk of Developing Learning Difficulties. Current Psychology, 31(1), 17–34. https://doi.org/10.1007/s12144-012-9128-3
Cowan, N., Bao, C., Bishop-Chrzanowski, B. M., Costa, A. N., Greene, N. R., Guitard, D., Li, C., Musich, M. L., & Ünal, Z. E. (2024). The Relation Between Attention and Memory. Annual Review of Psychology, 75(1), 183–214. https://doi.org/10.1146/annurev-psych-040723-012736
De Jong, T., Lazonder, A. W., Chinn, C. A., Fischer, F., Gobert, J., Hmelo-Silver, C. E., Koedinger, K. R., Krajcik, J. S., Kyza, E. A., Linn, M. C., Pedaste, M., Scheiter, K., & Zacharia, Z. C. (2023). Let’s talk evidence – The case for combining inquiry-based and direct instruction. Educational Research Review, 39, 100536. https://doi.org/10.1016/j.edurev.2023.100536
De Jong, T., & Van Joolingen, W. R. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179
DiDomenico, A., & Nussbaum, M. A. (2011). Effects of different physical workload parameters on mental workload and performance. International Journal of Industrial Ergonomics, 41(3), 255–260. https://doi.org/10.1016/j.ergon.2011.01.008
DiDomenico, A., & Nussbaum, M. A. (2008). Interactive effects of physical and mental workload on subjective workload assessment. International Journal of Industrial Ergonomics, 38(11–12), 977–983. https://doi.org/10.1016/j.ergon.2008.01.012
Dosher, B. A., Han, S., & Lu, Z.-L. (2010). Perceptual learning and attention: Reduction of object attention limitations with practice. Vision Research
Dube, B., Emrich, S. M., & Al-Aidroos, N. (2017). More than a filter: Feature-based attention regulates the distribution of visual working memory resources. Journal of Experimental Psychology: Human Perception and Performance, 43(10), 1843. 1843–1854. https://doi.org/10.1037/xhp0000428
Egeth, H. E., & Yantis, S. (1997). VISUAL ATTENTION: Control, Representation, and Time Course. Annual Review of Psychology, 48(1), 269–297. https://doi.org/10.1146/annurev.psych.48.1.269
Erickson, L. C., Thiessen, E. D., Godwin, K. E., Dickerson, J. P., & Fisher, A. V. (2015). Endogenously and exogenously driven selective sustained attention: Contributions to learning in kindergarten children. Journal of Experimental Child Psychology, 138, 126–134. https://doi.org/10.1016/j.jecp.2015.04.011
Febiyani, A., Febriani, A., & Ma’sum, J. (2021). Calculation of mental load from e-learning student with NASA TLX and SOFI method. Jurnal Sistem Dan Manajemen Industri, 5(1), 35–42. https://doi.org/10.30656/jsmi.v5i1.2789
Feisel, L. D., & Rosa, A. J. (2005). The Role of the Laboratory in Undergraduate Engineering Education. Journal of Engineering Education, 94(1), 121–130. https://doi.org/10.1002/j.2168-9830.2005.tb00833.x
Feldon, D. F., Callan, G., Juth, S., & Jeong, S. (2019). Cognitive Load as Motivational Cost. Educational Psychology Review, 31(2), 319–337. https://doi.org/10.1007/s10648-019-09464-6
Flavián, C., Ibáñez-Sánchez, S., & Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. Journal of Business Research, 100, 547–560. https://doi.org/10.1016/j.jbusres.2018.10.050
Gawlik-Kobylińska, M., Walkowiak, W., & Maciejewski, P. (2020). Improvement of a Sustainable World through the Application of Innovative Didactic Tools in Green Chemistry Teaching: A Review. Journal of Chemical Education, 97(4), 916–924. https://doi.org/10.1021/acs.jchemed.9b01038
George Saadé, R., Kira, D., & Nebebe, F. (2013). The Challenge of Motivation in e-Learning: Role of Anxiety. 301–308. https://doi.org/10.28945/1856
Geyer, T., Seitz, W., Zinchenko, A., Müller, H. J., & Conci, M. (2021). Why Are Acquired Search-Guiding Context Memories Resistant to Updating? Frontiers in Psychology, 12, 650245. https://doi.org/10.3389/fpsyg.2021.650245
Guha, P., Lawson, J., Minty, I., Kinross, J., & Martin, G. (2023). Can mixed reality technologies teach surgical skills better than traditional methods? A prospective randomised feasibility study. BMC Medical Education, 23(1), 144. https://doi.org/10.1186/s12909-023-04122-6
Guo, W., & Kim, J. H. (2020). How Augmented Reality Influences Student Workload in Engineering Education. In C. Stephanidis, D. Harris, W.-C. Li, D. D. Schmorrow, C. M. Fidopiastis, P. Zaphiris, A. Ioannou, X. Fang, R. A. Sottilare, & J. Schwarz (Eds.), HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games (Vol. 12425, pp. 388–396). Springer International Publishing. https://doi.org/10.1007/978-3-030-60128-7_29
Han, J., Liu, G., & Zheng, Q. (2023). Prior knowledge as a moderator between signaling and learning performance in immersive virtual reality laboratories. Frontiers in Psychology, 14, 1118174. https://doi.org/10.3389/fpsyg.2023.1118174
Hart, S. G., & Field, M. (2006). NASA-TASK LOAD INDEX (NASA-TLX); 20 YEARS LATER.
Hertzum, M. (2021). Reference values and subscale patterns for the task load index (TLX): A meta-analytic review. Ergonomics, 64(7), 869–878. https://doi.org/10.1080/00140139.2021.1876927
Hossain, Z., Bumbacher, E., Brauneis, A., Diaz, M., Saltarelli, A., Blikstein, P., & Riedel-Kruse, I. H. (2018). Design Guidelines and Empirical Case Study for Scaling Authentic Inquiry-based Science Learning via Open Online Courses and Interactive Biology Cloud Labs. International Journal of Artificial Intelligence in Education, 28(4), 478–507. https://doi.org/10.1007/s40593-017-0150-3
Huang, X., Yan, Z., Gong, C., Zhou, Z., Xu, H., Qin, C., & Wang, Z. (2023). A mixed-reality stimulator for lumbar puncture training: A pilot study. BMC Medical Education, 23(1), 178. https://doi.org/10.1186/s12909-023-04173-9
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Jamet, E. (2014). An eye-tracking study of cueing effects in multimedia learning. Computers in Human Behavior, 32, 47–53. https://doi.org/10.1016/j.chb.2013.11.013
Jamet, E., Gavota, M., & Quaireau, C. (2008). Attention guiding in multimedia learning. Learning and Instruction, 18(2), 135–145. https://doi.org/10.1016/j.learninstruc.2007.01.011
Jiang, J., & Fryer, L. K. (2024). The effect of virtual reality learning on students’ motivation: A scoping review. Journal of Computer Assisted Learning, 40(1), 360–373. https://doi.org/10.1111/jcal.12885
Johnson, D., Damian, D., & Tzanetakis, G. (2020). Evaluating the effectiveness of mixed reality music instrument learning with the theremin. Virtual Reality, 24(2), 303–317. https://doi.org/10.1007/s10055-019-00388-8
Johnston, W. A., & Dark, V. J. (1986). Selective attention. Annual Review of Psychology, 37, 43–75. https://doi.org/10.1146/annurev.ps.37.020186.000355
Jonassen, D. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (pp. 215-239). Lawrence Erlbaum Associates.
Jonassen, D. H., & Grabowski, B. L. H. (1993). Handbook of individual differences, learning and instruction. Erlbaum.
Jowsey, S., & Aguayo, C. (2017). O-tū-kapua (‘what clouds see’): A mixed reality experience bridging art, science, technology in meaningful ways. Teachers and Curriculum, 17(2).
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: from eye fixations to comprehension. Psychological review, 87(4), 329.
Kallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi‐structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031
Kalyuga, S. (Ed.). (2008). Managing cognitive load in adaptive multimedia learning. IGI Global.
Kangas, M., Siklander, P., Randolph, J., & Ruokamo, H. (2017). Teachers’ engagement and students’ satisfaction with a playful learning environment. Teaching and Teacher Education, 63, 274–284. https://doi.org/10.1016/j.tate.2016.12.018
Kantowitz, B. H. (2000). Attention and Mental Workload. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(21), 3-456-3–459. https://doi.org/10.1177/154193120004402121
Ke, F., Lee, S., & Xu, X. (2016). Teaching training in a mixed-reality integrated learning environment. Computers in Human Behavior, 62, 212–220. https://doi.org/10.1016/j.chb.2016.03.094
Kent, L., Snider, C., Gopsill, J., & Hicks, B. (2021). Mixed reality in design prototyping: A systematic review. Design Studies, 77, 101046. https://doi.org/10.1016/j.destud.2021.101046
Khacharem, A. (2017). Top-down and bottom-up guidance in comprehension of schematic football diagrams. Journal of Sports Sciences, 35(12), 1204–1210. https://doi.org/10.1080/02640414.2016.1218034
Kiili, K., Ketamo, H., & Kickmeier-Rust, M. D. (2014). Evaluating the usefulness of Eye Tracking in Game-based Learning. International Journal of Serious Games, 1(2). https://doi.org/10.17083/ijsg.v1i2.15
Kim, K.-J., Choi, M.-J., & Kim, K.-J. (2021). Effects of Nursing Simulation Using Mixed Reality: A Scoping Review. Healthcare, 9(8), 947. https://doi.org/10.3390/healthcare9080947
King, J., & Markant, J. (2022). Selective attention to lesson‐relevant contextual information promotes 3‐ to 5‐year‐old children’s learning. Developmental Science, 25(4), e13237. https://doi.org/10.1111/desc.13237
Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., Lee, M.-H., Chiou, G.-L., Liang, J.-C., & Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115. https://doi.org/10.1016/j.edurev.2013.10.001
Lew, S., Gul, T., & Pecore, J. L. (2021). ESOL pre-service teachers’ culturally and linguistically responsive teaching in mixed-reality simulations. Information and Learning Sciences, 122(1/2), 45–67. https://doi.org/10.1108/ILS-01-2020-0012
Liberatore, M. J., & Wagner, W. P. (2021). Virtual, mixed, and augmented reality: A systematic review for immersive systems research. Virtual Reality, 25(3), 773–799. https://doi.org/10.1007/s10055-020-00492-0
Lin, M.-H., Chen, H.-C., & Liu, K.-S. (2017). A Study of the Effects of Digital Learning on Learning Motivation and Learning Outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553–3564. https://doi.org/10.12973/eurasia.2017.00744a
Lin, Y.-G., McKeachie, W. J., & Kim, Y. C. (2003). College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences, 13(3), 251–258. https://doi.org/10.1016/S1041-6080(02)00092-4
Ling, C., & Salvendy, G. (2009). Effect of evaluators’ cognitive style on heuristic evaluation: Field dependent and field independent evaluators. International Journal of Human-Computer Studies, 67(4), 382–393. https://doi.org/10.1016/j.ijhcs.2008.11.002
Liu, M., & Reed, W. M. (1994). The relationship between the learning strategies and learning styles in a hypermedia environment. Computers in Human Behavior, 10(4), 419–434. https://doi.org/10.1016/0747-5632(94)90038-8
Longo, L., & Orrú, G. (2022). Evaluating instructional designs with mental workload assessments in university classrooms. Behaviour & Information Technology, 41(6), 1199–1229. https://doi.org/10.1080/0144929X.2020.1864019
López-Vargas, O., Ibáñez-Ibáñez, J., & Racines-Prada, O. (2017). Students’ metacognition and cognitive style and their effect on cognitive load and learning achievement. Journal of educational technology & society, 20(3), 145-157.
Lowe, R., & Ploetzner, R. (Eds.). (2017). Learning from Dynamic Visualization. Springer International Publishing. https://doi.org/10.1007/978-3-319-56204-9
Lowe, R. K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13(2), 157–176. https://doi.org/10.1016/S0959-4752(02)00018-X
Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220–234. https://doi.org/10.1080/10494820.2016.1278391
Ma, M., Fallavollita, P., Seelbach, I., Von Der Heide, A. M., Euler, E., Waschke, J., & Navab, N. (2016). Personalized augmented reality for anatomy education. Clinical Anatomy, 29(4), 446–453. https://doi.org/10.1002/ca.22675
Ma, X., Xie, Y., Yang, X., Wang, H., Li, Z., & Lu, J. (2024). Teacher-student interaction modes in smart classroom based on lag sequential analysis. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12487-4
Maas, M. J., & Hughes, J. M. (2020). Virtual, augmented and mixed reality in K–12 education: A review of the literature. Technology, Pedagogy and Education, 29(2), 231–249. https://doi.org/10.1080/1475939X.2020.1737210
Markant, J., & Amso, D. (2022). Context and attention control determine whether attending to competing information helps or hinders learning in school‐aged children. WIREs Cognitive Science, 13(1), e1577. https://doi.org/10.1002/wcs.1577
Markant, J., Worden, M. S., & Amso, D. (2015). Not all attention orienting is created equal: Recognition memory is enhanced when attention orienting involves distractor suppression. Neurobiology of Learning and Memory, 120, 28–40. https://doi.org/10.1016/j.nlm.2015.02.006
Matovu, H., Ungu, D. A. K., Won, M., Tsai, C.-C., Treagust, D. F., Mocerino, M., & Tasker, R. (2023). Immersive virtual reality for science learning: Design, implementation, and evaluation. Studies in Science Education, 59(2), 205–244. https://doi.org/10.1080/03057267.2022.2082680
Mayer, R. E. (2017). Using multimedia for e‐learning. Journal of Computer Assisted Learning, 33(5), 403–423. https://doi.org/10.1111/jcal.12197
McGarr, O. (2020). The use of virtual simulations in teacher education to develop pre-service teachers’ behaviour and classroom management skills: Implications for reflective practice. Journal of Education for Teaching, 46(2), 159–169. https://doi.org/10.1080/02607476.2020.1724654
Messick, S. (1984). The nature of cognitive styles: Problems and promise in educational practice. Educational Psychologist, 19(2), 59–74. https://doi.org/10.1080/00461528409529283
Milgram, P., & Kishino, F. (1994). A taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems, E77-D(12), 1321-1329.
Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry‐based science instruction—what is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474–496. https://doi.org/10.1002/tea.20347
Morris, B. J., Croker, S., Zimmerman, C., Gill, D., & Romig, C. (2013). Gaming science: The “Gamification” of scientific thinking. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00607
Moro, C., Phelps, C., Redmond, P., & Stromberga, Z. (2021). HoloLens and mobile augmented reality in medical and health science education: A randomised controlled trial. British Journal of Educational Technology, 52(2), 680–694. https://doi.org/10.1111/bjet.13049
Müller, C., Krone, M., Huber, M., Biener, V., Herr, D., Koch, S., Reina, G., Weiskopf, D., & Ertl, T. (2018). Interactive Molecular Graphics for Augmented Reality Using HoloLens. Journal of Integrative Bioinformatics, 15(2), 20180005. https://doi.org/10.1515/jib-2018-0005
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
Ng, C. (2019). Shifting the focus from motivated learners to motivating distributed environments: A review of 40 years of published motivation research in Distance Education. Distance Education, 40(4), 469–496. https://doi.org/10.1080/01587919.2019.1681892
Nobre, A. C. (Kia), & Kastner, S. (2014). Attention (A. C. (Kia) Nobre & S. Kastner, Eds.; Vol. 1). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199675111.013.040
Oakes, L., & Amso, D. (2018). Development of Visual Attention. In J. T. Wixted (Ed.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience (1st ed., pp. 1–33). Wiley. https://doi.org/10.1002/9781119170174.epcn401
OECD. (2019). PISA 2018 Results (Volume I): What Students Know and Can Do. OECD. https://doi.org/10.1787/5f07c754-en
Ogunseiju, O., Gonsalves, N. J., Akanmu, A. A., & Bairaktarova, D. (2024). Towards Personalized Mixed Reality-Based Learning Experience in Construction Education. In Digitalization in Construction (pp. 20-38). Routledge.
Palmer, S. R., & Holt, D. M. (2009). Examining student satisfaction with wholly online learning.Journal of Computer Assisted Learning, 25(2), 101–113. https://doi.org/10.1111/j.1365-2729.2008.00294.x
Pan, D., Zhang, Y., & Li, Z. (2016). Predictive capability of cognitive ability and cognitive style for spaceflight emergency operation performance. International Journal of Industrial Ergonomics, 54, 48–56. https://doi.org/10.1016/j.ergon.2016.04.008
Pan, Z., Luo, T., Zhang, M., Cai, N., Li, Y., Miao, J., Li, Z., Pan, Z., Shen, Y., & Lu, J. (2022). MagicChem: A MR system based on needs theory for chemical experiments. Virtual Reality, 26(1), 279–294. https://doi.org/10.1007/s10055-021-00560-z
Panagiotidis, P. (2021). Augmented and Mixed Reality in Language Learning. European Journal of Education, 4(2), 27–43. https://doi.org/10.26417/501ibq23c
Parveau, M., & Adda, M. (2018). 3iVClass: A new classification method for Virtual, Augmented and Mixed Realities. Procedia Computer Science, 141, 263–270. https://doi.org/10.1016/j.procs.2018.10.180
Pedaste, M., Mäeots, M., Siiman, L. A., De Jong, T., Van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61. https://doi.org/10.1016/j.edurev.2015.02.003
Pellas, N., Kazanidis, I., & Palaigeorgiou, G. (2020). A systematic literature review of mixed reality environments in K-12 education. Education and Information Technologies, 25(4), 2481–2520. https://doi.org/10.1007/s10639-019-10076-4
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning. Ann Arbor: University of Michigan.
Price *, L. (2004). Individual Differences in Learning: Cognitive control, cognitive style, and learning style. Educational Psychology, 24(5), 681–698. https://doi.org/10.1080/0144341042000262971
Qin, Y., Zhang, W., Zhao, C., Wang, Z., Zhu, X., Shi, J., Qi, G., & Lei, Z. (2021). Prior-knowledge and attention based meta-learning for few-shot learning. Knowledge-Based Systems, 213, 106609. https://doi.org/10.1016/j.knosys.2020.106609
Rayner, K. (1998). Eye Movements in Reading and Information Processing: 20 Years of Research. EYE MOVEMENTS IN READING.
Reiss, M. J., Millar, R., & Osborne, J. (1999). Beyond 2000: Science/biology education for the future. Journal of Biological Education, 33(2), 68–70. https://doi.org/10.1080/00219266.1999.9655644
Risko, E. F., & Kingstone, A. (2011). Eyes wide shut: Implied social presence, eye tracking and attention. Attention, Perception, & Psychophysics, 73(2), 291–296. https://doi.org/10.3758/s13414-010-0042-1
Serrano-Mamolar, A., Miguel-Alonso, I., Checa, D., & Pardo-Aguilar, C. (2023). Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning. Comunicar, 31(76). https://doi.org/10.3916/C76-2023-01
S. Piro, J., & O’Callaghan, C. (2019). Journeying Towards the Profession: Exploring Liminal Learning within Mixed Reality Simulations. Action in Teacher Education, 41(1), 79–95. https://doi.org/10.1080/01626620.2018.1534221
Shing, Y. L., & Brod, G. (2016). Effects of Prior Knowledge on Memory: Implications for Education. Mind, Brain, and Education, 10(3), 153–161. https://doi.org/10.1111/mbe.12110
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Song, H. S., Kalet, A. L., & Plass, J. L. (2016). Interplay of prior knowledge, self‐regulation and motivation in complex multimedia learning environments. Journal of Computer Assisted Learning, 32(1), 31–50. https://doi.org/10.1111/jcal.12117
Sonntag, D., & Bodensiek, O. (2022). How mixed reality shifts visual attention and success in experimental problem solving. Physical Review Physics Education Research, 18(2), 023101. https://doi.org/10.1103/PhysRevPhysEducRes.18.023101
Sterr, A. M. (2004). Attention performance in young adults with learning disabilities. Learning and Individual Differences, 14(2), 125–133. https://doi.org/10.1016/j.lindif.2003.10.001
Strzys, M. P., Kapp, S., Thees, M., Klein, P., Lukowicz, P., Knierim, P., Schmidt, A., & Kuhn, J. (2018). Physics holo.lab learning experience: Using smartglasses for augmented reality labwork to foster the concepts of heat conduction. European Journal of Physics, 39(3), 035703. https://doi.org/10.1088/1361-6404/aaa8fb
Su, C.-H. (2016). The effects of students’ motivation, cognitive load and learning anxiety in gamification software engineering education: A structural equation modeling study. Multimedia Tools and Applications, 75(16), 10013–10036. https://doi.org/10.1007/s11042-015-2799-7
Sun, J. C.-Y., & Hsu, K. Y.-C. (2019). A smart eye-tracking feedback scaffolding approach to improving students’ learning self-efficacy and performance in a C programming course. Computers in Human Behavior, 95, 66–72. https://doi.org/10.1016/j.chb.2019.01.036
Szpiro, S. F. A., & Carrasco, M. (2015). Exogenous Attention Enables Perceptual Learning. Psychological Science, 26(12), 1854–1862. https://doi.org/10.1177/0956797615598976
Taheri, A., & Aguayo, C. (2021). Embodied immersive design for experience-based learning and self-illumination. In LINK 3rd Conference in Practice-Oriented Research in Art & Design, 2(1), 365–366. School of Art and Design, AUT. https://doi.org/10.24135/link2021.v2i1.72
Tan, Z., Robinson, H. L., Yin, D.-M., Liu, Y., Liu, F., Wang, H., Lin, T. W., Xing, G., Gan, L., Xiong, W.-C., & Mei, L. (2018). Dynamic ErbB4 Activity in Hippocampal-Prefrontal Synchrony and Top-Down Attention in Rodents. Neuron, 98(2), 380-393.e4. https://doi.org/10.1016/j.neuron.2018.03.018
Tang, Y. M., Au, K. M., Lau, H. C. W., Ho, G. T. S., & Wu, C. H. (2020). Evaluating the effectiveness of learning design with mixed reality (MR) in higher education. Virtual Reality, 24(4), 797–807. https://doi.org/10.1007/s10055-020-00427-9
Tesfamariam, G. M., Lykknes, A., & Kvittingen, L. (2017). ‘Named Small but Doing Great’: An Investigation of Small-Scale Chemistry Experimentation for Effective Undergraduate Practical Work. International Journal of Science and Mathematics Education, 15, 393-410.
Thomas, P. R., & McKay, J. B. (2010). Cognitive styles and instructional design in university learning. Learning and Individual Differences, 20(3), 197–202. https://doi.org/10.1016/j.lindif.2010.01.002
Tiselius, E., & Sneed, K. (2020). Gaze and eye movement in dialogue interpreting: An eye-tracking study. Bilingualism: Language and Cognition, 23(4), 780–787. https://doi.org/10.1017/S1366728920000309
Tolentino, L., Birchfield, D., Megowan-Romanowicz, C., Johnson-Glenberg, M. C., Kelliher, A., & Martinez, C. (2009). Teaching and Learning in the Mixed-Reality Science Classroom. Journal of Science Education and Technology, 18(6), 501–517. https://doi.org/10.1007/s10956-009-9166-2
Tu, Y. P., & Chi, M. (Eds.). (2024). E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future: 23rd Wuhan International Conference, WHICEB 2024, Wuhan, China, May 24–26, 2024, Proceedings, Part III (Vol. 517). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-60324-2
Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97-136. https://doi.org/10.1016/0010-0285(80)90005-5
Traxler, J., & Wishart, J. M. (2011). Making mobile learning work: Case studies of practice (Discussion Papers in Education). ESCalate, HEA Subject Centre for Education. http://escalate.ac.uk/8250
Tsai, M.-J., Wu, A.-H., Bråten, I., & Wang, C.-Y. (2022). What do critical reading strategies look like? Eye-tracking and lag sequential analysis reveal attention to data and reasoning when reading conflicting information. Computers & Education, 187, 104544. https://doi.org/10.1016/j.compedu.2022.104544
Tsai, M.-J., Huang, L.-J., Hou, H.-T., Hsu, C.-Y., & Chiou, G.-L. (2016). Visual behavior, flow and achievement in game-based learning. Computers & Education, 98, 115–129. https://doi.org/10.1016/j.compedu.2016.03.011
Ubuz, B., & Aydınyer, Y. (2019). Project-based geometry learning: Knowledge and attitude of field-dependent/independent cognitive style students. The Journal of Educational Research, 112(3), 285–300. https://doi.org/10.1080/00220671.2018.1502138
Valdez, M. T., Ferreira, C. M., Martins, M. J. M., & Barbosa, F. P. M. (2015). 3D virtual reality experiments to promote electrical engineering education. 2015 International Conference on Information Technology Based Higher Education and Training (ITHET), 1–4. https://doi.org/10.1109/ITHET.2015.7217957
Vallée, A., Blacher, J., Cariou, A., & Sorbets, E. (2020). Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis. Journal of Medical Internet Research, 22(8), e16504. https://doi.org/10.2196/16504
Van Marlen, T., Van Wermeskerken, M., Jarodzka, H., & Van Gog, T. (2018). Effectiveness of eye movement modeling examples in problem solving: The role of verbal ambiguity and prior knowledge. Learning and Instruction, 58, 274–283. https://doi.org/10.1016/j.learninstruc.2018.07.005
Van De Laar, P., Heskes, T., & Gielen, S. (1997). Task-Dependent Learning of Attention. Neural Networks, 10(6), 981–992. https://doi.org/10.1016/S0893-6080(97)00031-2
Van Ede, F., & Nobre, A. C. (2023). Turning Attention Inside Out: How Working Memory Serves Behavior. Annual Review of Psychology, 74(1), 137–165. https://doi.org/10.1146/annurev-psych-021422-041757
Van Gog, T., & Jarodzka, H. (2013). Eye Tracking as a Tool to Study and Enhance Cognitive and Metacognitive Processes in Computer-Based Learning Environments. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (Vol. 28, pp. 143–156). Springer New York. https://doi.org/10.1007/978-1-4419-5546-3_10
Vicente Dos Anjos, F. E., Rocha, L. A. O., Oliveira Da Silva, D., & Pacheco, R. (2021). Impacts of the Application of Virtual and Augmented Reality on Teaching-Learning Processes in Engineering Courses: A Systematic Literature Review About Learning and Satisfaction on Students. International Journal of Virtual and Personal Learning Environments, 12(1), 1–19. https://doi.org/10.4018/IJVPLE.291541
Vu, T., Magis-Weinberg, L., Jansen, B. R. J., Van Atteveldt, N., Janssen, T. W. P., Lee, N. C., Van Der Maas, H. L. J., Raijmakers, M. E. J., Sachisthal, M. S. M., & Meeter, M. (2022). Motivation-Achievement Cycles in Learning: A Literature Review and Research Agenda. Educational Psychology Review, 34(1), 39–71. https://doi.org/10.1007/s10648-021-09616-7
Wang, Q., & Li, Y. (2024). How virtual reality, augmented reality and mixed reality facilitate teacher education: A systematic review. Journal of Computer Assisted Learning, jcal.12949. https://doi.org/10.1111/jcal.12949
Wang, Z., Chen, Z., Gong, B., & Feng, Z. (2024). The interactive effects of instructors’ guidance frequency and type on Chinese secondary school students’ learning. Education and Information Technologies, 29(9), 11257–11280. https://doi.org/10.1007/s10639-023-12148-y
Wetzels, S. A. J., Kester, L., & Van Merriënboer, J. J. G. (2011). Adapting prior knowledge activation: Mobilisation, perspective taking, and learners’ prior knowledge. Computers in Human Behavior, 27(1), 16–21. https://doi.org/10.1016/j.chb.2010.05.004
Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66(1), 55–84. https://doi.org/10.1016/j.cogpsych.2012.09.002
Witkin, H. A., & Goodenough, D. R. (1981). Cognitive styles: Essence and origins. New York, NY: International Universities Press.
Witkin, H. A., Moore, C. A., Goodenough, D., & Cox, P. W. (1977). Field-Dependent and Field-Independent Cognitive Styles and Their Educational Implications. Review of Educational Research, 47(1), 1–64. https://doi.org/10.3102/00346543047001001
Wolfe, J. M. (2010). Visual search. Current Biology, 20(8), R346-R349.
Wu, I.-C., & Yu, H.-K. (2020). Sequential analysis and clustering to investigate users’ online shopping behaviors based on need-states. Information Processing & Management, 57(6), 102323. https://doi.org/10.1016/j.ipm.2020.102323
Wu, R., & Zhao, J. (2017). Prior Knowledge of Object Associations Shapes Attentional Templates and Information Acquisition. Frontiers in Psychology, 8, 843. https://doi.org/10.3389/fpsyg.2017.00843
Xie, H., Zhao, T., Deng, S., Peng, J., Wang, F., & Zhou, Z. (2021). Using eye movement modelling examples to guide visual attention and foster cognitive performance: A meta‐analysis. Journal of Computer Assisted Learning, 37(4), 1194–1206. https://doi.org/10.1111/jcal.12568
Yang, F.-Y., & Wang, H.-Y. (2023). Tracking visual attention during learning of complex science concepts with augmented 3D visualizations. Computers & Education, 193, 104659. https://doi.org/10.1016/j.compedu.2022.104659
Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643–650. https://doi.org/10.1016/j.neucom.2015.09.081
Yoon, G. S. (1994). The effects of instructional control, cognitive style, and prior knowledge on learning of computer-assisted instruction. Journal of Educational Technology Systems, 22(4), 357-370.
Yu, X., Zhou, Z., Becker, S. I., Boettcher, S. E. P., & Geng, J. J. (2023). Good-enough attentional guidance. Trends in Cognitive Sciences, 27(4), 391–403. https://doi.org/10.1016/j.tics.2023.01.007
Yu, Q. (2022). Factors Influencing Online Learning Satisfaction. Frontiers in Psychology, 13, 852360. https://doi.org/10.3389/fpsyg.2022.852360
Zakaria, Z., Latip, J., & Tantayanon, S. (2012). Organic Chemistry Practices for Undergraduates using a Small Lab Kit. Procedia - Social and Behavioral Sciences, 59, 508–514. https://doi.org/10.1016/j.sbspro.2012.09.307
Zhang, B., Zhang, J. X., Huang, S., Kong, L., & Wang, S. (2011). Effects of load on the guidance of visual attention from working memory. Vision Research, 51(23–24), 2356–2361. https://doi.org/10.1016/j.visres.2011.09.008
Zhang, Y., Meyers, E. M., Bichot, N. P., Serre, T., Poggio, T. A., & Desimone, R. (2011). Object decoding with attention in inferior temporal cortex. Proceedings of the National Academy of Sciences, 108(21), 8850–8855. https://doi.org/10.1073/pnas.1100999108
Zheng, W., Jia, L., Sun, N., Liu, Y., Geng, J., & Zhang, D. (2022). Effects of Attention Direction and Perceptual Distraction Within Visual Working Memory. Frontiers in Psychology, 13, 801252. https://doi.org/10.3389/fpsyg.2022.801252 |
| Description: | 碩士
國立政治大學
圖書資訊與檔案學研究所
111155010 |
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