Search

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow. Lay Description: What is already known about this topic: Researchers have made extensive use of data from online learning platforms—often referred to as learning trace data—to understand how learners engage in online learning activities.Surveys are often used to gather information about learners' motivation, perceptions, and other internal factors, to complement the insight gleaned from learning traces and thus create a more complete picture of the learning behaviour.When analysing survey data, the focus is usually on individual factors without considering how different factors are connected and affect one another.Psychological networks analysis is a novel analytic approach to studying complex phenomena both in psychology and education. What this paper adds: The paper uses psychological networks as a method for examining mutual relations among learners' internal factors (e.g., motivation, perceptions) measured through self‐reports before and after a course.By combining conventional statistical analysis of self‐reports with psychological networks analysis, the paper develops a comprehensive picture of learners' internal factors measured through self‐reports, one that accounts for both individual and interconnected nature of those factors.This comprehensive approach to information extraction from surveys allows for better understanding of trace‐based learner profiles, that is, profiles mined from data about learners' interaction with online learning activities. Implications for practice and/or policy: The presented method paves a way for a comprehensive analysis of the data collected from learners through surveys.It helps us gain a better understanding of learners' engagement with online learning activities.It can advance the evaluation of digital educational programs that aim to encourage changes in health‐related behaviour. [ABSTRACT FROM AUTHOR]

This paper introduces a unified conceptual framework for understanding refugee identity construction within educational contexts, drawing on theories of social justice (Fraser, 2008), affective relations (Lynch, 2012), the resumption of ordinary life (Kohli, 2011) and ecological systems (Bronfenbrenner, 1977). Addressing a gap in the existing scholarship, the model emphasises the importance of affective relations in shaping refugee identities in education alongside other factors. The study examines the macro constructs of redistribution, recognition, representation and relational affect, highlighting their impact on refugee children's educational experiences. Further, it considers the micro constructs of safety, sense of belonging and success in integration and attainment in educational environments. The constructs are examined across various levels of Bronfenbrenner's ecological system, demonstrating the complexity of refugees' needs. This model aims to facilitate a holistic, justice‐oriented approach to refugee education, which is crucial given the rise in global displacement. The study has significant implications for refugee education research and policymaking, potentially informing targeted interventions and programmes that foster academic success, overall well‐being, and practice in refugee education. [ABSTRACT FROM AUTHOR]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

The objective of the paper is to present a methodology that can be used to translate a model from one formalism to another allowing model reuse and cross‐verification. With the use of formal languages, the model specifications can be expressed in a rigorous and univocal way, and the model can be validated against the specifications or conceptual model. Expressing the same model in different formal languages opens the conceptual model validation to a varied number of specialists. Also, in the context of modeling a new system, where there is no data to perform validation against a real system, having pairs of models can be used to perform cross‐model verification to ensure that the model specifications or conceptual models are correctly implemented by comparing the results produced by both models. Specifically, a method is presented to translate a model conceptualized on specification and description language (SDL) to discrete event system specification (DEVS). The transformation mechanism between SDL and DEVS formalisms is described. The methodology is exemplified with a disease spread model for COVID‐19, and it is shown how the results obtained by the two models can be used for cross‐verification. [ABSTRACT FROM AUTHOR]

As full AI‐based automation remains out of reach in most real‐world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human‐AI systems. Our conceptual model is centered around three high‐level aspects ‐ agency, interaction, and adaptation ‐ and is developed through a multi‐step process. Firstly, an initial design space is proposed by surveying the literature and consolidating existing definitions and conceptual frameworks. Secondly, this model is iteratively refined and validated by conducting semi‐structured interviews with nine researchers in this field. Lastly, to illustrate the applicability of our design space, we utilize it to provide a structured description of selected human‐AI systems. [ABSTRACT FROM AUTHOR]

Topic: Early‐life experiences, the transmission of health and disease within families, and the influence of cumulative risks as well as protective factors throughout life shape the trajectory of health, including mental health. Long‐term health trajectories established early in life are influenced by biologic, social, and environmental factors. Negative trajectories may be more salient if exposures to adversity occur during critical developmental periods. Purpose: The purpose of this brief is to (a) review pediatric health disparities related to depression and the intergenerational transmission of pediatric depression using a Life Course Health Development (LCHD) model and (b) provide recommendations for pediatric mental health research. Sources: Peer‐reviewed papers available for PubMed, CINAL, and Medline. Other sources include published books, papers, and gray materials. Conclusions: The LCHD model is a perspective to guide and foster new scientific inquiry about the development of mental health outcomes over the life course. The model enables synthesis of mental health, nursing, and public health, linking mental health prevention, risk reduction, and treatment in children. [ABSTRACT FROM AUTHOR]

Given that the number of children and young people needing care keeps rising and fewer people are becoming foster carers, efforts to support carers and workers in foster caring are essential. This paper considers the experiences of carers and foster care agency workers involved in Australia's piloting of the Mockingbird Family. With a view understanding experience, data were collected via focus groups with carers and agency workers (n = 20) involved in piloting, implementation and evaluation. Deductive analysis applied the theory of experience to generate understanding of experience, as both intrinsic and extrinsic dimensions to capture strengths in the Mockingbird Family's foster caring networks. These dimensions of experience included collective passions of carers and workers; experiential change over time; collective experiences as a moving force; and experiences as transformational. Understanding of experience associated with the perceived strengths of the Mockingbird Family, including strategies to promote strong professional relationships between carers and workers, is an important element in strengthening environments of children and young people in care. Safe and stable environments are crucial for wellbeing. [ABSTRACT FROM AUTHOR]

This paper explores the components of Chinese college counsellors' mental models in their daily work based on a rich content analysis of the story accounts of 152 exemplary college counsellors over the past 8 years. On the basis of a rigid three‐step coding procedure, this study reveals that Chinese college counsellors' mental models generally comprise four common constituents, namely a love‐filled faith system, a rule‐based thinking system, a professionalism‐based knowledge system and action‐based performance. These components jointly illustrate the general landscape of Chinese college counsellors' internal well‐being and fill a research gap regarding the sparse attention given to counsellors in the global literature on faculty professionalism. [ABSTRACT FROM AUTHOR]

Romantic relationships are more satisfying and fulfilling when power is balanced relatively equally between partners (Leonhardt et al., Journal of Family Psychology, 34, 2020, and 1). Yet, few couples therapy models explicitly outline how to confront relational power issues (Knudson‐Martin & Huenergardt, 2015, Socio‐emotional relationship therapy: Bridging emotion, societal context, and couple interaction, Springer). Emotionally Focused Therapy (EFT; Johnson, 2020, The practice of emotionally focused couple therapy, Routledge) is a well‐established, evidence‐based therapy modality that many therapists use with couple clients, yet despite its effectiveness, it does not provide direction for explicitly addressing and treating power differentials in couple relationships. In this paper, we explore the integration of EFT with Socio‐emotional Relationship Therapy (SERT), a model overlay that acknowledges the impact of social discourse on enactments of power in intimate couple relationships. We first address the importance of understanding power in couple relationships, addressing power in couples therapy, and provide a brief overview of SERT and EFT. We then introduce an integration of the models intended to help therapists balance power, increase connection, and secure attachment bonds between romantic partners. [ABSTRACT FROM AUTHOR]

Polythermal glaciers can trap considerable volumes of liquid water with the potential to generate devastating outburst floods. This study aims to identify water‐filled subglacial reservoirs from ambient seismic noise collected by moderate‐cost surveys. The horizontal‐to‐vertical spectral ratio technique is highly sensitive to impedance contrasts at interfaces, thus commonly used to estimate glacier thickness. Here, we focus on the inverse ratio, that is, the V/H spectral ratio (VHSR), whose high values indicate a low impedance volume beneath the surface, suggesting subglacial cavities. We analyze VHSR peaks from a seismic array of 60 nodes installed on the Tête‐Rousse Glacier (Mont Blanc massif, French Alps); data were gathered over 15 days. Mapping the VHSR amplitude over the free surface reveals the main cavity locations and the basal areas affected by melting within the glacier. Results obtained in the field are supported by a conceptual model based on 3D finite‐element simulations. Plain Language Summary: Considerable volumes of liquid water may be trapped within cavities in polythermal glaciers. If these cavities rupture, the resulting outburst flood has the potential to cause devastation in populated mountain areas. With the aim of testing methods to locate such cavities, we installed 60 small 3‐component seismic sensors on the Tête‐Rousse Glacier (Mont Blanc massif, French Alps), which is known to contain such cavities. We used these sensors to test a detection method based on ambient seismic noise. For 3 weeks, the sensors recorded vibrations within the glacier. On a glacier without cavities, these vibrations ought to be predominantly in the horizontal direction. In the presence of a cavity, we expect the ice above the cavity to vibrate mostly vertically—like a bridge. In this paper, we highlight areas on the glacier where vertical vibrations were stronger than horizontal vibrations. These areas fit well with the locations of the main known cavities in this glacier, and with areas affected by basal melting. We supported our field observations with modeling based on 3D simulations, paving the way to a new method to locate water‐filled cavities within glaciers. Key Points: Spectral analysis from ambient seismic noise is complementary to other geophysical methods for investigating glaciers at depthResults suggest that the vertical‐to‐horizontal spectral ratio is a reliable proxy to locate subglacial cavitiesExperimental results were confirmed using a simplified numerical model [ABSTRACT FROM AUTHOR]