Uncovering video-based learning processes: Cued-retrospective reporting versus concurrent and retrospective reporting

Learning was dominated by online form during the COVID-19 outbreak. In the future, the learning process may remain in distance learning or hybrid. The popular platform in distance learning are videos and online meetings. This study aims to compare the forms of learning that are more attractive to students in distance learning. The learning format that will be compared is video-based learning (VBL) and online meetings. The method used in this research is descriptive quantitative, using a questionnaire distributed to electronic engineering students. This study explores six aspects of student interest in distance learning: convenience, accessibility, understanding, interaction, learning outcomes, and flexibility. The results of this research show that in terms of comfort, interactiveness, and learning outcomes, the online meeting is more attractive to students than VBL. Moreover, VBL is considered better in terms of accessibility, understanding and flexibility. Although both VBL and online meetings excel in 3 aspects, the survey results show that students are generally more interested in online meetings. The conclusion of this study shows that students are more interested in online meeting forms for several reasons than video-based learning, but both forms have positive and negative sides.

Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video‐based learning (VBL), tracking real‐time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning process. This study proposed and validated an electroencephalogram (EEG)‐supported approach to tracking real‐time cognitive load variations during continuous VBL. We recruited 108 healthy adult participants to watch a specially designed video lecture with a sequence of interconnected slides of equal length. EEG signals were continuously recorded throughout the session. The video lecture was designed with varying levels of content difficulty (ie, rated from 1 to 5) across slides and was narrated at three different speeds (ie, slow, normal and fast) to induce cognitive load variations. For each slide, the cognitive load was quantified using both subjective ratings (ie, self‐reported difficulty) and an EEG‐derived measure (ie, alpha power). Through linear mixed model analysis, we demonstrated the feasibility of using alpha power to track real‐time cognitive load variations during the continuous VBL process after controlling the effect of mental fatigue. This study provides a foundation for developing learning enhancement technologies that enable the timely provision of adaptive learning support in VBL. Practitioner notesWhat is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations.

A video-based learning (VBL) has long practically been used in the education environment to help students enhance their level of understanding. Educational content for VBL has been widely produced on various platforms to support achieving the optimum level of student performance. Additionally, the concept of edutainment using video in education is gaining traction, particularly in children's learning. Implementing the edutainment notion into the learning process is a critical component of successful learning. Previous research has examined the beneficial effects of edutainment using video for learning purposes. It has been shown to improve the effectiveness of the learning process and generate enjoyable learning experiences for students. As the paper is aimed at children, the EMV can capture their attention even more than a standard educational video does due to its interactive content. However, studies on edutainment for educational music videos, in particular, have not been studied much. Thus, the objective of this paper is two-fold; i) to review and discuss the conceptual approach of edutainment and its significance towards student learning experiences, and ii) to propose the component and element of edutainment in the educational music video (EMV) for children learning. The findings of this study outline the characteristics of edutainment that can be utilised as criteria for selecting the finest EMV in learning for children.

In the learning process in elementary schools, obstacles in learning are very often found. Obstacles can make it difficult for students to understand the material delivered by the teacher because the explanations given are too abstract so motivation to study the human digestive system is still relatively low. The aim of this research is to increase students' motivation to learn. Science material on the human digestive system through video-based learning. This research utilized a descriptive research design and qualitative methodology. With three data acquisition techniques, namely documentation, interviews and observation. Based on motivation, research shows that the use of video-based learning media can convey the content of the learning process very clearly so as to increase students' motivation to learn. The encouragement for student learning is by using video-based learning media, the availability of educational materials can make everything simpler. in conveying the content of the learning that students will learn. It is hoped that the use of the designed teaching videos can increase students' learning motivation in learning the science of the human digestive system.

This study aims to design educational videos as video-based learning and determine the design that makes students more interested in paying attention to the distance learning process. If students want to pay attention to the material during the learning process, students will also get the maximum learning experience. The study was focused on the Department of Fashion at SMKN 1 Dawuan Subang to improve clarity. To obtain empirical data, this study used quantitative comparative with data collection using questionnaires with census sampling. The number of respondents is 22 students. The steps are input measurement design video with Bandicam and output measurement obtained from the evaluation results based on three elements for video design and implementation: cognitive load, student engagement, and active learning together. The data collection technique used a questionnaire. The results indicate that video-based learning using Bandicam was most effective in organizing tempo, but more attention must be paid to segmentation because the visualization video was formal and tended to be static. Further research should explore video-based learning using more engaging and dynamic applications like Prezi or similar tools.

The rapid development of digital technology has revolutionized the educational landscape, which demands more innovative and engaging teaching methods. One such method is Video-Based Learning (VBL), which utilizes multimedia resources, such as videos, to enhance students' learning experience. This article discusses the application of VBL as a strategy to enhance student engagement in the classroom in the context of the digital age. The research examines the effectiveness of VBL in encouraging active participation, enhancing understanding and strengthening students' motivation to learn. By integrating video content in the learning process, educators can provide a dynamic and interactive learning environment that suits various learning styles. In addition, VBL facilitates access to resources beyond traditional teaching, allowing students to interact with learning materials according to their pace and preferences. This research demonstrates the positive impact of VBL on student engagement, especially in subjects that benefit from visual and auditory stimulation, and discusses challenges and solutions in effective implementation. This article aims to provide insights and practical guidelines for educators in integrating Video-Based Learning in their teaching practices, so that students are better equipped to thrive in the digital age.

Eye tracking technology has become more popular recently for understanding video-based learning processes.The main purpose of this research is to provide an overview of eye tracking research in video-based learning. For this purpose, 172 eye-tracking articles related to video-based learning indexed in the Web of Science Core Collection were analyzed. A bibliometric approach was applied to reveal the intellectual nature of the research. VOSviewer software was used for bibliometric analysis. The results showed that "eye tracking" was the keyword with the highest frequency of occurrence. The most influential authors are Jarodzka, H., and Van Gog, T. Most of the studies reviewed were conducted in the USA, Germany, and China. It has been determined that the "Research in Developmental Disabilities" journal stands out regarding the number of documents in the journals, and the "Computers & Education" journal ranks first in the number of citations. The results provide insights into a recent research on eye tracking in video-based learning processes. It is thought that this study will help researchers see trends in the field and guide future studies and applications.

The learning model used greatly affects the learning process in the Covid-19 pandemic era. The online learning that has been passed in this one year has caused boredom. The learning process is too monotonous, the teacher's intonation is less varied, and not easy to interact directly with friends and teachers. Therefore, to achieve an effective and maximum learning process, the researcher proposes using video-based learning and gamification methods to increase deeper understanding of the material, motivation in learning, and student involvement in the learning process through the Learning Management System (LMS). The material presented will be transformed into more interactive and interesting videos such as simple animated videos, tutorial videos, podcast videos, and others. This research aims to provide positive benefits for students to be more active in discussing and collaborating and enthusiastic in doing all learning activities. The test to measure the level of motivation and involvement can be carried out in three stages, namely with pre-test and post-test, T-test and analytical data from student access to the LMS according to the indicators involved in this study such as video completion, total video, total comments, total badges, and completion of the game level. This study result indicates a positive influence from the application of video-based learning and gamification methods on LMS to increase student motivation and engagement.

The use of video is only one tool that can enhance students' learning experience in the field of physical education. From the explanation above, it can be concluded that the integration of learning media in the learning process itself cannot be released, so efforts are needed to produce varied learning media and can be used to support the learning process. The integration of learning media in the physical education learning process cannot be ignored, so efforts are needed to produce varied learning media and can be used to support the learning process. This study aims to produce physical education learning videos using the ADDIE model starting from analyze, design, development, implementation, and evaluation. The data were analyzed by qualitative descriptive and quantitative descriptive techniques. The results showed that the assessment by material experts received a 90% rating (very good), then media experts got 83% (good) and user responses got 85% (good). Furthermore, the calculation of the influence of video on learning outcomes has an effect size of 1.09 which is included in the high-influence category.. The use of video as support should still pay attention to direct interaction between teachers and students, as well as opportunities to practice and participate actively. The integration of learning media in the learning process cannot be ignored, with the integration proven to have a positive impact on learning outcomes.Key words: Video-based learning, physical education, learning media.

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.

Video-based learning has become a popular, scalable, and effective approach for students to learn new skills. Many of the challenges for video-based learning can be addressed with machine learning models. However, the available datasets often lack the rich source of data that is needed to accurately predict students’ learning experiences and outcomes. To address this limitation, we introduce the MSP-GEO corpus, a new multimodal database that contains detailed demographic and educational data, recordings of the students and their screens, and meta-data about the lecture during the learning experience. The MSP-GEO corpus was collected using a quasi-experimental pre-test/post-test design. It consists of more than 39,600 seconds (11 hours) of continuous facial footage from 76 participants watching one of three experimental videos on the topic of fossil formation, resulting in over one million facial images. The data collected includes 21 gaze synchronization points, webcam and monitor recordings, and metadata for pauses, plays, and timeline navigation. Additionally, we annotated the recordings for engagement, boredom, and confusion using human evaluators. The MSP-GEO corpus has the potential to improve the accuracy of video-based learning outcomes and experience predictions, facilitate research on the psychological processes of video-based learning, inform the design of instructional videos, and advance the development of learning analytics methods.

The aim of this study is to investigate students from non-major English department perspectives toward the use of Video-Based Learning in an online English language process. This study was conducted at Politeknik Negeri Ujung Pandang using a convergent-parallel design, a combination of quantitative and qualitative methods. Eighty students of the first semester from the accounting department who were involved in a video-based learning process became the participants of this study. The data was gathered through the use of a questionnaire that included closed-ended and open-ended questions. The data from the closed-ended questions were quantitatively analyzed using descriptive statistics in SPSS. Additionally, a narrative method was used to analyze the open-ended questions. The results of this study indicate that the non-English major students have a positive perspective toward video-based learning utilization because it increases their motivation to learn and makes it easier for them to comprehend the material. Keywords: Video-Based Learning, Non-English Major Students’ Perspectives, Online Class

The aim of the study was to examine the effectiveness of a model called VBL: Video-Based Learning. VBL is designed to improve the learning of higher education courses, especially those based on activities performed on a computer screen, or learning related to the understanding of visual objects such as formulas, equations, diagrams, etc. The present study aims to examine the effectiveness of the model for quantitative courses such as statistics, mathematics, computer courses or equivalent. The research was based on six samples of students (n1 =14, n2 =41, n3 =17, n4 =27, n5 =27, n6 =19, n-total =145) who studied three quantitative courses: Fundamentals of PSPP, Introduction to Statistics and Math for Business Administration. The learners were asked to answer an online questionnaire to assess the characteristics and advantages of VBL for their studies. The findings of the study indicate that, according to the students' perceptions, VBL has a significant advantage for students' learning in quantitative courses in higher education: The learning process is outstanding and is much better than reading texts or listening to live lectures and it is considerably flexible. Therefore, it is recommended to adopt the model in faculties of higher education teaching quantitative courses.

This study explores the use of bilingualism in teaching English to Young learners through video-based learning. By integrating videos as a medium, the learning process becomes more interactive, visually appealing, and contextually rich. The study investigates how bilingual videos—featuring explanations, dialogues, and visual aids in both the students' native language and English—enhance vocabulary acquisition, pronunciation, and listening skills. Results indicate that bilingual video-based learning improves understanding and retention, particularly for young learners in a multilingual environment. This approach offers a practical, engaging strategy for early English education, bridging linguistic barriers while cultivating an interest in language learning.

Video-based learning has successfully become an effective alternative to face-to-face instruction. In such situations, modeling or predicting learners’ flow experience during video learning is critical for enhancing the learning experience and advancing learning technologies. In this study, we set up an instructional scenario for video learning according to flow theory. Different learning states, i.e., boredom, fit (flow), and anxiety, were successfully induced by varying the difficulty levels of the learning task. We collected learners’ electrocardiogram (ECG) signals as well as facial video, upper body posture and speech data during the learning process. We proposed classification models of the learning state and regression models to predict flow experience by utilizing different combinations of the data from the four modalities. The results showed that the model performance of learning state recognition was significantly improved by the decision-level fusion of multimodal data. By using the selected important features from all data sources, such as the standard deviation of normal to normal R-R intervals (SDNN), high-frequency (HF) heart rate variability and mel-frequency cepstral coefficients (MFCC), the multilayer perceptron (MLP) classifier gave the best recognition result of learning states (i.e., mean AUC of 0.780). The recognition accuracy of boredom, fit (flow) and anxiety reached 47.48%, 80.89% and 47.41%, respectively. For flow experience prediction, the MLP regressor based on the fusion of two modalities (i.e., ECG and posture) achieved the optimal prediction (i.e., mean RMSE of 0.717). This study demonstrates the feasibility of modeling and predicting the flow experience in video learning by combining multimodal data.