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She L, Wang Z, Tao X, Lai L. The Impact of Color Cues on the Learning Performance in Video Lectures. Behav Sci (Basel) 2024; 14:560. [PMID: 39062383 PMCID: PMC11274038 DOI: 10.3390/bs14070560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 06/10/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information. Evaluating 78 college students, we assessed learning performance by comparing video content with no-color, single-color, and multi-color cues using eye-tracking technology and cognitive load scales. Results indicate that students viewing videos with color cues demonstrated better retention and transfer test performance, while absence or excess of color cues increased cognitive load. These findings have practical implications for video producers and provide a theoretical foundation for enhancing learners' viewing experience and overall effectiveness. This study not only offers an in-depth analysis of color cue utilization in video lectures, highlighting their positive impact on learning outcomes but also introduces fresh perspectives for educational technology and cognitive psychology research. Future investigations should consider color cue effects in diverse cultural contexts and subject areas, exploring varied strategies to optimize the learning experience.
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Affiliation(s)
- Linwei She
- International Business School, Jinan University, Zhuhai 519070, China
| | - Zhiguo Wang
- Network and Educational Technology Center, Jinan University, Guangzhou 510632, China
| | - Xiaohui Tao
- International Business School, Jinan University, Zhuhai 519070, China
| | - Liqi Lai
- Modern Education Technology Centre, Jinan University, Zhuhai 519070, China
- GBA and B&R International Joint Research Center for Smart Logistics, Jinan University, Zhuhai 519070, China
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Marcantoni I, Assogna R, Del Borrello G, Di Stefano M, Morano M, Romagnoli S, Leoni C, Bruschi G, Sbrollini A, Morettini M, Burattini L. Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5968. [PMID: 37447818 DOI: 10.3390/s23135968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/18/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.
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Affiliation(s)
- Ilaria Marcantoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Raffaella Assogna
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Giulia Del Borrello
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Marina Di Stefano
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Martina Morano
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Sofia Romagnoli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Chiara Leoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Micaela Morettini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
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Jamil N, Belkacem AN, Lakas A. On enhancing students' cognitive abilities in online learning using brain activity and eye movements. EDUCATION AND INFORMATION TECHNOLOGIES 2023; 28:4363-4397. [PMID: 36277512 PMCID: PMC9574174 DOI: 10.1007/s10639-022-11372-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/19/2022] [Indexed: 05/11/2023]
Abstract
The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.
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Affiliation(s)
- Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
| | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
| | - Abderrahmane Lakas
- Department of Computer and Network Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
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Guo X, Zhu T, Wu C, Bao Z, Liu Y. Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals. Front Psychol 2022; 13:889427. [PMID: 35769742 PMCID: PMC9236132 DOI: 10.3389/fpsyg.2022.889427] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning.
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Affiliation(s)
| | | | | | | | - Yang Liu
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
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