Learners’ confusion: faulty prior knowledge or a metacognitive monitoring error?

Research often treats confusion as a turning point of the learners’ cognitive-affective dynamics in digital environments (e.g. D’Mello, Grasser and colleagues). The origin of confusion, however, is a topic of a debate. Could inaccurate prior knowledge serve as a source of confusion, or does confusion relate to metacognitive processes? In this paper we are attempting to address this question by employing case study analysis with fourteen participants who worked through simulated learning problems with feedback in a digital environment. Physiological and self-reported data were combined to examine problem-solving patterns. Preliminary findings highlighted the role of metacognitive monitoring in confusion development and its interrelation with inaccurate prior knowledge.5 page(s

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning

This article reviews studies investigating segmentation of dynamic visualizations (i.e., showing dynamic visualizations in pieces with pauses in between) and discusses two not mutually exclusive processes that might underlie the effectiveness of segmentation. First, cognitive activities needed for dealing with the transience of dynamic visualizations impose extraneous cognitive load, which may hinder learning. Segmentation may reduce the negative effect of this load by dividing animations into smaller units of information and providing pauses between segments that give students time for the necessary cognitive activities after each of those units of information. Second, event segmentation theory states that people mentally segment dynamic visualizations during perception (i.e., divide the information shown in pieces). Segmentation of dynamic visualisation could cue relevant segments to students, which may aid them in perceiving the structure underlying the process or procedure shown

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Investigating the use of sensor-based IoET to facilitate learning for children in rural Thailand

A novel sensor-based Internet of Educational Things (IoET) platform named OBSY was iteratively designed, developed and evaluated to support education in rural regions in Thailand. To assess the effectiveness of this platform, a study was carried out at four primary schools located near the Thai northern border with 244 students and 8 teachers. Participants were asked to carry out three science-based learning activities and were measured for improvements in learning outcome and learning engagement. Overall, the results showed that students in the IoET group who had used OBSY to learn showed significantly higher learning outcome and had better learning engagement than those in the control condition. In addition, for those in the IoET group, there was no significant effect regarding gender, home location (Urban or Rural), age, prior experience with technology and ethnicity on learning outcome. For learning engagement, only age was found to influence interest/enjoyment. The study demonstrated the potential of IoET technologies in underprivileged area, through a co-design approach with teachers and students, taking into account the local contexts

La compréhension d'un document technique multimédia peut-elle être améliorée par une présentation séquentielle de son contenu ?

International audienc

Can the comprehension of a multimedia technical document be improved by a sequential presentation of its content?

18 page(s

Identifying epistemic emotions from activity analytics in interactive digital learning environments

This volume is split into five sections, all of which relate to the key themes in understanding learning analytics through the lens of the classroom: broad theoretical perspectives understanding learning through analytics the relationship ..