Cognitive and affective processes for learning science in immersive virtual reality

Emotions can both facilitate and hinder learning. Emotional design features such as colors and shapes can be embedded in multimedia learning environments to manipulate learners’ affects and learning outcomes. However, some studies suggest that emotional designs promote learning, while others show that they hinder it. Although Brom et al. (Educational Research Review 25:100–119, Brom et al. 2018) published a meta-analysis on the use of emotional designs in multimedia learning, an updated search showed that more studies were published recently. Thus, the present meta-analysis is a replication and extension of Brom et al.’s (Educational Research Review 25:100–119, Brom et al. 2018) meta-analysis. A total of 28 articles yielded the following independent effect sizes for each outcome examined: retention (k = 28), transfer (k = 38), comprehension (k = 16), mental effort (k = 28), perceived difficulty (k = 19), change in positive affect (k = 25), intrinsic motivation (k = 28), and liking/enjoyment (k = 19). Results showed that including emotional designs enhanced learning outcomes (retention: g+ = 0.35; transfer: g+ = 0.27; comprehension: g+ = 0.29), change in positive affect (g+ = 0.09), intrinsic motivation (g+ = 0.15), mental effort (g+ = 0.11), liking/enjoyment (g+ = 0.10), and reduced perceived difficulty (g+ = − 0.21). Moderator analyses were conducted for retention, mental effort, intrinsic motivation, and positive affect, and findings showed that mean effect sizes were moderated by participant characteristics as well as methodological and contextual features of the studies. We discuss these findings as well as their theoretical and practical implications.

Meta-Analysis of Emotional Designs in Multimedia Learning: A Replication and Extension Study

Collaborative learning activities have become a popular method in online education to encourage active learning and equip students with team working skills that are highly valued by employers. However, past research has identified that working with other students online has the potential to cause anxiety for learners, particularly when work is being assessed. There is concern that, as well as the emotional distress this may cause, anxiety may affect students' participation and performance in these activities. This paper investigates these issues by exploring part-time distance learners' experiences of a group project where they were required to collaborate online to create a wiki resource and a website. An online survey and interviews were conducted with students who had recently completed the project. Results revealed that anxiety was commonly experienced, and causes of anxiety included relying on ‘unknown others’, fear of negative evaluation, and worries about non-active group members. It was found that anxiety reduced over the course of the project, as feelings of uncertainty were resolved. Findings also revealed that, although anxiety is often viewed to have detrimental consequences, more learners perceived anxiety to have had a facilitative effect on participation and performance than a debilitative one. Students who employed problem-focused coping strategies, rather than avoidance coping ones, were more likely to experience facilitative effects. These findings will be of value to educators who are designing and running online collaborative activities, and students who are participating in them.

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Students’ experiences of anxiety in an assessed, online, collaborative project

Analyzing students’ emotions from classroom videos can help both teachers and parents quickly know the engagement of students in class. The availability of high-definition cameras creates opportunities to record class scenes. However, watching videos is time-consuming, and it is challenging to gain a quick overview of the emotion distribution and find abnormal emotions. In this article, we propose EmotionCues , a visual analytics system to easily analyze classroom videos from the perspective of emotion summary and detailed analysis, which integrates emotion recognition algorithms with visualizations. It consists of three coordinated views: a summary view depicting the overall emotions and their dynamic evolution, a character view presenting the detailed emotion status of an individual, and a video view enhancing the video analysis with further details. Considering the possible inaccuracy of emotion recognition, we also explore several factors affecting the emotion analysis, such as face size and occlusion. They provide hints for inferring the possible inaccuracy and the corresponding reasons. Two use cases and interviews with end users and domain experts are conducted to show that the proposed system could be useful and effective for analyzing emotions in the classroom videos.

EmotionCues : Emotion-Oriented Visual Summarization of Classroom Videos

Micro-learning is an educational teaching method used to train users on multiple platforms. This article will provide a brief introduction to the concepts of short-term and long-term memory, and explain how micro-learning can be used to increase retention in learners. Micro-lessons can aid in negating the Ebbinghaus forgetting curve and can use reintroduction to keep retention at significantly higher levels. This process also speeds up the learning process overall because students avoid the phenomenon of mental fatigue. The article cites studies suggesting mental fatigue can cause serious cognitive decline in individual performance. By breaking complex courses into manageable smaller lessons, micro-learning preserves the neurotransmitter cascade for steady neurochemical performance. By using mobile devices, students can pause and continue their micro-lessons with ease. The mobile application also gives them the opportunity to continually check on their performance, and adjust their learning accordingly. Micro-learning on mobile devices also keeps engagement levels high because it utilizes different forms of media to keep users captivated.

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Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature.

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional "live" learning in classrooms and "virtual" learning through online-based educational technologies.

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The Influences of Emotion on Learning and Memory.

This paper aims to analyze the neuronal behavior of the brain during learning of science complex concepts and compare the brain dynamics during learning state with resting state. A sample of 34 healthy participants was recruited and performed two tasks, Raven's Advanced Progressive Matrices (RAPM) intelligence assessment and learning task. Electroencephalography (EEG) signals were recorded during resting state (eyes open) and in learning task. In the learning task, participants study animated contents about human anatomy and physiology for 10 minutes. In the EEG spectral analysis, all six frequency bands showed higher mean power during learning task compared to resting state in the frontal lobe, especially in FP2 and F8 locations. High EEG activity in frontal sites reflects high attention demand and working memory resources for studying the complex science concepts. There is also high connectivity between fronto-temporo-parietal regions indicate that an increase connection network for new memory encoding and it is critical for learning and memory.

EEG spectral analysis and functional connectivity during learning of science concepts

Subcutaneous veins localization is basic and important step for any intravenous medication administration. Due to different physiological characteristics, mainly darker skin tone, scars or dehydrated condition of patients, medical staff face difficulty in veins localization. Through near infrared imaging technology the veins can be visualized due to high contrast between veins and skin tissue in this modality. Information on the depth of veins is equally important for proper catheterization or venipuncture procedures. Patients have different veins depth due to the different amount of fat present in the subcutaneous layer. The depth of veins from the skin surface cannot be estimated by simple imaging technique. In this paper a mathematical model to estimate the depth of veins based on measured diffused reflectance is presented. A layered model of Monte Carlo simulations for light transport in turbid medium was used to validate the results.

Subcutaneous veins depth estimation method using Monte Carlo simulations

This study aims to examine the brain dynamics in terms of EEG activity in multimedia learning using coherence and source localization analysis. EEG recording was performed on 34 participants. EEG coherence was computed over 171 pairs of electrodes across both hemispheres in different frequency bands and Low-Resolution Electromagnetic Tomography (LORETA) was applied for source analysis. The EEG coherence results showed that higher functional interaction exists between anterior-posterior long-range coherence for all four frequency bands (delta, theta, alpha and beta) for both within left and right hemispheres compared with anterior and posterior short-range coherence. Further, source localization analysis demonstrated that a significant increase in delta and theta at the bilateral frontal lobes as well as in beta activity at the left temporal lobe; while a significant decrease of alpha over right parietal, beta over frontal and posterior parieto-occipital regions during learning process. To conclude, the frontal lobe is highly recruited in multimedia learning compared with baseline eyes open. In addition, it is also actively interacting with temporal lobe and posterior parieto-occipital regions to facilitate perceptual, encoding and retrieval of prior knowledge during learning.

EEG coherence and source localization analysis during multimedia learning process

The soaring of diabetes cases in Malaysia has resulted in the appearance of diabetic retinopathy among diabetic patients. Diabetic retinopathy is a chronic eye disease triggered by diabetes, which could worsen eyesight functions and even blindness. Even though the cases were found to be common, medical experts still diagnose the disease manually, which increase the risk of incorrect diagnosis. To overcome this, the preliminary study on severity levels classifications of diabetic retinopathy from fundus images has been conducted by applying a deep learning model. A Convolutional Neural Network (CNN) deep learning model architecture is used to train the dataset, which is DenseNet. Various image pre-processing techniques have been applied to enhance the trained images. Moreover, data augmentation and test-time augmentation (TTA) are implemented in evaluating the training results and lower the overfitting, respectively. Prediction evaluation on the images and the effects of data augmentation and TTA by observing the quadratic weighted kappa values were conducted. Ultimately, a prediction model that is to predict and classify the severity labels of fundus images was developed. The prediction model achieved the quadratic weighted kappa score of 0.9308, with the accuracy of 65% on the Messidor-2 dataset, which were moderately accurate.

Chai Meei Tyng

Papers

The Influences of Emotion on Learning and Memory.

EEG spectral analysis and functional connectivity during learning of science concepts

Subcutaneous veins depth estimation method using Monte Carlo simulations

EEG coherence and source localization analysis during multimedia learning process

Preliminary Study of Diabetic Retinopathy Classification from Fundus Images Using Deep Learning Model