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Analytics of Self-Regulated Learning in Learning Analytics Feedback Processes:Associations With Feedback Literacy in Secondary Education
No full textBackground: A key skill for self-regulated learners is the ability to critically interpret and act on feedback—key components of feedback literacy. Yet, the connection between feedback literacy and self-regulated learning (SRL) remains underexplored, particularly in terms of how different levels of feedback literacy influence SRL processes in authentic learning contexts. Objectives: This study aimed to investigate the interplay between feedback literacy and SRL processes among secondary school students while working on a multi-source writing task using an online learning analytics (LA) platform. Methods: The study involved 99 secondary school students from multiple nations (Brazil, UAE, India, and Australia) engaged in multi-source writing tasks. Students received personalised scaffolding feedback designed to enhance their SRL processes. Using K-medoids clustering, students were grouped based on their self-reported feedback literacy levels. Ordered Network Analysis (ONA) was employed to visualise and analyse trace data from the learning analytics platform, revealing SRL strategies across different feedback literacy profiles. Results and Conclusions: Analysis of data from the 99 participants revealed two distinct feedback literacy groups with different SRL patterns. Proactive Feedback Engagers (n = 55) initially showed less effective SRL strategies but demonstrated significant improvement after receiving scaffolding interventions, adopting more balanced and adaptive regulation processes. In contrast, Moderate Feedback Engagers (n = 48) began with a more strategic approach but showed less adaptability as the task progressed, diverging from suggested SRL processes. These findings imply the need for more adaptive scaffolding approaches based on students' feedback literacy levels, highlighting the importance of tailored support in developing learning strategies based on different levels of feedback literacy.</p
Relational Cybercrimes:A New Way Forward in Classifying Cybercrimes
No full textCybercrimes are classified as either cyber-dependent or cyber-enabled. We present an alternative classification by introducing a cluster referred to as relational cybercrimes—which includes offences, such as romance scams, cyberstalking, sextortion, and radicalization—hoping this classification may lead to prevention breakthroughs.</p
Managing technical debt in a multidisciplinary data intensive software team:An observational case study
No full textContext: There is an increase in the investment and development of data-intensive (DI) solutions — systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD). Delivery of DI solutions requires a multidisciplinary skill set, but there is limited knowledge about how multidisciplinary teams develop DI systems and manage TD. Objective: This research contributes empirical, practice based insights about multidisciplinary DI team TD management practices. Method: This research was conducted as an exploratory observation case study. We used socio-technical grounded theory (STGT) for data analysis to develop concepts and categories that articulate TD and TDs debt management practices. Results: We identify TD that the DI team deals with, in particular technical data components debt and pipeline debt. We explain how the team manages the TD, assesses TD, what TD treatments they consider and how they implement TD treatments to fit sprint capacity constraints. Conclusion: We align our findings to existing TD and TDM taxonomies, discuss their implications and highlight the need for new implementation patterns and tool support for multidisciplinary DI teams.</p
Equivalence assessment of weight bearing cone beam CT and multidetector CT through 3D Knee bone modelling
No full textWeight-bearing cone beam computed tomography (WB-CBCT, or simply WBCT), which captures high-resolution 3D images in a natural standing position, has gained increasing interest in recent years. This study examines the potential of WBCT as an alternative to multidetector computed tomography (MDCT) for 3D bone modelling. We generated 3D knee joint models from manually annotated WBCT and MDCT scans, performed rigid registration of these models, and assessed their similarity by evaluating the mean difference, standard deviation, and confidence intervals of the aligned models. The mean differences were computed as the average surface distances between corresponding WBCT and MDCT 3D bone models after rigid registration, providing a quantitative measure of their geometric similarity. Validation was conducted using both patient and cadaver scans to assess WBCT’s clinical applicability under realistic conditions and its technical reliability with controlled samples. Our findings reveal an average absolute difference of less than 0.35 mm for patient scans and 0.30 mm for cadaveric scans between WBCT and MDCT. The patella demonstrated the smallest mean difference (-0.20 mm to 0.10 mm) and standard deviation (0.28 mm to 0.55 mm) across all scans. These results confirm the comparability of WBCT to MDCT for 3D bone modelling, highlighting WBCT’s capacity to deliver appropriate image quality for the clinical assessment of bone joints.</p
Contrastive Learning-Based Multi-Level Knowledge Distillation
No full textWith the increasing constraints of hardware devices, there is a growing demand for compact models to be deployed on device endpoints. Knowledge distillation, a widely used technique for model compression and knowledge transfer, has gained significant attention in recent years. However, traditional distillation approaches compare the knowledge of individual samples indirectly through class prototypes overlooking the structural relationships between samples. Although recent distillation methods based on contrastive learning can capture relational knowledge, their relational constraints often distort the positional information of the samples leading to compromised performance in the distilled model. To address these challenges and further enhance the performance of compact models, we propose a novel approach, termed contrastive learning-based multi-level knowledge distillation (CLMKD). The CLMKD framework introduces three key modules: class-guided contrastive distillation, gradient relation contrastive distillation, and semantic similarity distillation. These modules are effectively integrated into a unified framework to extract feature knowledge from multiple levels, capturing not only the representational consistency of individual samples but also their higher-order structure and semantic similarity. We evaluate the proposed CLMKD method on multiple image classification datasets and the results demonstrate its superior performance compared to state-of-the-art knowledge distillation methods.</p
Job crafting through the lens of exploitation and exploration:a daily diary study on job crafting towards strengths and development
No full textThis study investigates how employees engage in two distinct job crafting strategies by either leveraging their existing strengths (job crafting towards strengths, JC-strengths) or pursuing personal development (job crafting towards development, JC-development) through the lens of exploitation and exploration. We propose that JC-strengths, as an exploitative strategy, enhances task performance, whereas JC-development, as an explorative strategy, boosts creative performance. We further propose that job autonomy enables both JC-strengths and JC-development by affording discretion in how work is shaped, while a strong performance-pay link serves as a directional signal by reinforcing exploitation-oriented crafting (JC-strengths) and discouraging exploration-oriented crafting (JC-development) in the presence of job autonomy. Conducting a 10-day daily survey among 115 employees, our findings confirmed the hypothesized distinct effects of JC-strengths and JC-development on task and creative performance on a daily basis, respectively. Moreover, daily job autonomy was found to be significantly related to daily JC-strengths, especially when coupled with a high performance-pay link. However, the expected effect of daily job autonomy on daily JC-development and the cross-level moderating effect of performance-pay link on this relationship were not significant.</p
Bridging digital finance and ESG success:the role of financing constraints, innovation, and governance
No full textThis study investigates the impact of digital finance on corporate ESG performance, using panel data from A-share listed companies on the Shanghai and Shenzhen stock markets between 2011 and 2022. Our findings demonstrate that digital finance significantly enhances corporate ESG outcomes, with financing constraints and digital transformation serving as partial mediators and internal control quality acting as a moderating factor. The results from channel tests indicate that digital finance facilitates notable improvements in social performance and corporate governance, while its influence on environmental performance remains limited. Further analysis reveals that the positive impacts of digital finance on ESG are more evident in small-scale, technology-intensive, and non-polluting firms. This study concludes by proposing tailored recommendations for government, financial institutions, and corporations, emphasizing the need for differentiated policies to elevate ESG practices and promote higher quality, sustainable economic, and social development in China.</p
Content moderation and community standards:the disconnect between policy and user experiences reporting harmful and offensive content on social media
No full textModerating harmful and offensive content on social media is challenging for digital platforms that seek to balance regulation and censorship across a diverse user group. It is further complicated by discrepancies between platform policies, user expectations and user experiences. Informed by focus groups with 104 Australians aged 13–74 years, and a walkthrough analysis of three social media platforms, this article examines users' experiences reporting harmful and offensive content on Facebook, Instagram and X. It explores user expectations and the complexities that arise when platforms claim to maintain Community Standards, while prioritising (only some) users' freedom of expression and wellbeing. It argues that existing platform policies and content moderation relating to reporting harmful and offensive content are inconsistent, contradictory and non-transparent, and this is most acutely felt by users from marginalised communities who are most likely to be the targets of harmful and offensive content. We argue that moderation of harmful and offensive content should expand beyond binary retaining or removal. Rather, it could utilise innovative, context and socially-specific prospective outcomes, which could contribute towards user autonomy, freedom of expression, shared community learning of social norms and a reduction in the silencing of marginalised users.</p
Immigrant clockmakers in eighteenth-century Paris
No full textIn the eighteenth century, Paris was a center of innovation in clock- and watchmaking, and much of this was due to immigrant craftsmen, mostly from other parts of Europe. My paper asks why this was so. It argues that the prominence of outsiders resulted largely from the encounter between the experience of migration and local conditions in eighteenth-century Paris. Many talented immigrants came from places with traditions of mobility and were keen to learn and to succeed. Rather than bringing new or superior skills, they exploited possibilities offered by the French capital, in particular the ability to work in certain “privileged” locations and the rewards for innovation offered by the French government and the Academy of Sciences. In Paris, they encountered a market where demand for quality and novelty was high, and an expanding trade that had a culture of innovation
Combating confirmation bias:a unified pseudo-labeling framework for entity alignment
No full textEntity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. It has been a compelling but challenging task that requires the integration of heterogeneous information from different KGs to expand the knowledge coverage and enhance inference abilities. To circumvent the shortage of prior seed alignments provided for training, recent EA models utilize pseudo-labeling strategies to iteratively add unaligned entity pairs predicted with high confidence to the seed alignments for model training. However, the adverse impact of confirmation bias during pseudo-labeling has been largely overlooked, thus hindering entity alignment performance. To systematically combat confirmation bias, we propose a new Unified Pseudo-Labeling framework for Entity Alignment (UPL-EA) that explicitly alleviates pseudo-labeling errors to boost the performance of entity alignment. UPL-EA achieves this goal through two key innovations: (1) Optimal Transport (OT)-based pseudo-labeling uses discrete OT modeling as an effective means to determine entity correspondences and reduce erroneous matches across two KGs. An effective criterion is derived to infer pseudo-labeled alignments that satisfy one-to-one correspondences; (2) Parallel pseudo-label ensembling refines pseudo-labeled alignments by combining predictions over multiple models independently trained in parallel. The ensembled pseudo-labeled alignments are thereafter used to augment seed alignments to reinforce subsequent model training for alignment inference. The effectiveness of UPL-EA in eliminating pseudo-labeling errors is both theoretically supported and experimentally validated. Our extensive results and in-depth analyses demonstrate the superiority of UPL-EA over 15 competitive baselines and its utility as a general pseudo-labeling framework for entity alignment.</p