Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. (Educational Psychology Review, 10, 251–296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.
Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a `heuristic' that suits the current network. In this paper, we study this heuristic learning paradigm for link prediction. First, we develop a novel $\gamma$-decaying heuristic theory. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. Our results show that local subgraphs reserve rich information related to link existence. Second, based on the $\gamma$-decaying theory, we propose a new algorithm to learn heuristics from local subgraphs using a graph neural network (GNN). Its experimental results show unprecedented performance, working consistently well on a wide range of problems.
For several styles of fidelity constraints -- guaranteed distortion, conditional excess distortion, excess distortion -- we show mutual information upper bounds on the minimum expected description length needed to represent a random variable. Coupled with the corresponding converses, these results attest that as long as the information content in the data is not too low, minimizing the mutual information under an appropriate fidelity constraint serves as a reasonable proxy for the minimum description length of the data. We provide alternative characterizations of all three convex proxies, shedding light on the structure of their solutions.
Krisztina Napsugár Nagy, Csilla Evelin Károlyi, László Bakacsy
Abstract This study examined the ecological impact of the invasive shrub Amorpha fruticosa in marsh meadows and assessed the effectiveness of combined conservation management practices, i.e., mowing and cattle grazing, in on restoring native vegetation. Conducted in the Mártély Landscape Protection Area (Hungary), the research used a multiscale approach to compare treated non-invaded and treated invaded wetland vegetation. Information theory-based diversity metrics were employed to evaluate the impact of A. fruticosa on structural complexity and species composition. Results revealed that although A. fruticosa can significantly altered plant community structure, the implemented management strategies effectively reduced its impact. The treated invaded vegetation exhibited diversity levels compareable to native marshland communities, suggesting that mowing and grazing contributed to decreasing A. fruticose dominance. However, in the absence of untreated control stands, this pattern must be interpreted cautiously, as the observed similarity could be conclusively attributed to the applied management alone. A slight, but non-significant shift in structural diversity was also observed, implying a residual effect of invasion. This study underscores the value of active, combined conservation strategies in maintaining biodiversity and ecosystem resilience in wetland habitats. The results contribute to broader discussions on invasive species control, emphasizing the role of traditional land-use practices in mitigating the ecological effects of biological invasions.
The theory of functional sentence perspective (FSP) is centred on communicative dynamism and its distribution among communicative units, i.e. individual sentence elements (Firbas, 1996; 1999). When a context-dependent subject is further specified by more dynamic elements, the sentence follows the Quality Scale; conversely, if a context-independent subject is the most dynamic element, it follows the Presentation Scale. This corpus-based study, building on Adam & Headlandová Kalischová (2009), examines English sentences with prepositional predications of the pattern BE + PREPOSITIONAL PHRASE (specifically, be at fault, be at large, be in full swing, be in place, be on guard, be on display). Unlike the 2009 study, which categorized these structures based on semantic interpretation and paraphrasing potential, the present analysis explores their textual, syntactic, and information-structure characteristics irrespective of typological classification. The aim is to determine whether, and under what circumstances, these predicates express existence or appearance on the scene. The findings suggest that the FSP status of verbo-nominal prepositional structures is best understood as a continuum, ranging from predominantly presentational to primarily qualitative, rather than a strict binary categorization.
Background During the COVID-19 pandemic, we developed an e-learning resource to support medical students in having effective conversations with COVID vaccine hesitant patients (an urgent challenge). Development of information and communication skills elements was underpinned by narrative learning theory; learners interact with three fictional characters whose stories run through the resource in activities and role-plays. We co-developed the resource and characters with students, colleagues and local community. Methods We used a mixed methods approach to evaluate the resource, including a survey of pre- and post- module self-confidence scores, and by thematic analysis of a focus group with seven final year medical students to explore their perceptions of how the story elements influenced their learning. Results All students surveyed reported an improvement in their confidence in having effective conversations with vaccine-hesitant patients. The focus group analysis suggests that character-based narratives can promote learning online, particularly through engagement and accessibility, relatability of characters and their stories, improved memory, and emotional connection. Conclusions This study suggests that character-driven stories have potential value in online learning about vaccine hesitancy conversations. Further research is needed to establish the nature of their impact on different aspects of learning including the duration of effect on students' communication skills and any patient-related outcomes.
In physics, it is crucial to identify operational measurement procedures to give physical meaning to abstract quantities. There has been significant effort to define time operationally using quantum systems, but the same has not been achieved for space. Developing an operational procedure to obtain information about the location of a quantum system is particularly important for a theory combining general relativity and quantum theory, which cannot rest on the classical notion of spacetime. Here, we take a first step towards this goal, and introduce a model to describe an extended material quantum system working as a position measurement device. Such a "quantum ruler" is composed of $N$ harmonically interacting dipoles and serves as a (quantum) reference system for the position of another quantum system. We show that we can define a quantum measurement procedure corresponding to the "superposition of positions", and that by performing this measurement we can distinguish when the quantum system is in a coherent or incoherent superposition in the position basis. The model is fully relational, because the only meaningful variables are the relative positions between the ruler and the system, and the measurement is expressed in terms of an interaction between the measurement device and the measured system.
Social media networks are expanding rapidly, increasing the spread and scale of information diffusion. Researchers have highlighted distinguishing features of social media network platforms: network structure transparency, public self-monitored digital profiles, homogenized network connections, and node-created digital content. These features, while adding utility to social media platform providers and users, can also be exploited to manipulate users’ behavior and overall network outcomes. This paper posits the importance of network theory as critical foundational “laws” upon which the responsible innovation of social media can be built to minimize such manipulation, how such theory can be used to predict the potential impacts of new network innovations, and the resulting difficulty this framing suggests for self-governance on the part of platform providers. Through a case study analysis of Russian social media interference in the 2016 U.S. presidential election, the value of a network theoretic lens is highlighted. The concept of “supranodes”, social media nodes empowered via theoretical knowledge and network awareness to socially engineer network structures and outcomes, is developed and the network theoretic features they exploit discussed.
Aim and Introduction: The main purpose of this study is the meta-synthesis of effective signaling to internal and external stakeholders. Signaling theory is trying to determine the important role of signaling by different players in changing the economic results through making more information asymmetry (Connelly et al., 2011). On the other hand, a stakeholder is an entity whose decisions affect the company and vice versa (Beringer et al., 2013). In this study, two types of stakeholders are considered: internal and external. The study is developmental, qualitative in terms of data nature, and documentary-meta-synthesis in terms of data collection.Methodology: In this research CASP- Critical Assessment Skills Program is used for evaluating articles. For 72 selected articles from valid scientific databases from 1996 to 2021, the process of extracting information was performed from the results and analysis of these articles. After evaluating each of them, finally, 48 articles were approved and screened by 10 qualitative criteria (Sandelowski & Barroso, 2006). By examining the backgrounds and models of the approved articles, a conceptual model is resulted based on the achieved codes and categories.In this part, there are three different phases: combining the findings, quality control, and reporting the final results. The purpose of meta-synthesis is to build the cohere and new interpretation for results. This method has been used for clearing up the concepts and patterns in accepted references (Kant, 2006); because of that, all findings in this process are considered as codes and themes which are mentioned totally in the paper.Findings: The result of the analysis of approved papers’ models and contents, based on the methodology above, appears in 4 categories and 27 codes. The main categories are signal, effective signal attributes, internal stakeholders, and external stakeholders. Social responsibility, product quality, and product innovation are the most repetitive signals, which mentioned in researches. Other signals can be set to receivers like process innovation, marketing innovation, consequence, commitment, and intent. In the signaling attribute category, signal observability is the main code, which can define the effectiveness of signal and signaling in general. On the other hand, in the internal stakeholder category, staff and in external stakeholders, competitors, business partners, and customers are the main signal receivers. Signal and signaling attributes impact signaling effectiveness: signal cost, observability, fit, frequency, validity, and consistency. Among these attributes, observability has a major impact on signaling effectiveness. Finally, the conceptual model which contains signals, their attributes, and their main receivers is formed based on analysis.Discussion and Conclusion: The company gives different signals to its target audience with its different actions and chosen signaling mix. Depending on the company's signaling goals and the company’s signaling mix, the receivers of the signals can be a group of internal and external stakeholders of the company. According to research articles, the focus is more on some kind of signals and also some stakeholders as receivers; For example, companies generally target their external stakeholders, especially the customer and competitor (Talke & O'Connor,2011). On the other hand, the repetition of signaling to internal stakeholders is less among the studies. By reviewing the contents of the company’s signaling portfolio, the most emphasis in studies is on social responsibility, reliability, innovativeness, and quality of the company and its products.The emphasis of this study is on effective signaling to stakeholders, because the company tries to signal the true quality of it and the next reason is decreasing the gap between perceived signals and intended ones to get to its signaling goals. The characteristics of the signals and the signaling process, in general, can determine the effectiveness of the signals and signaling itself. That group of characteristics includes transparency pf signal, the validity of signal, stability of the signal, signal cost (Certo & et al.,2003), signal fit, and signal frequency (Carter, 2006) which the signal transparency and observability have got the most weight between others (Ramaswami et al., 2010).For further researches, researchers can consider some suggested subjects by authors in this very area: identifying other elements affecting the effectiveness of signals and signaling besides the mentioned elements like the characteristics of the signal sender, the characteristics of signal receivers, signaling environment, chosen signaling mix, framing the proper signaling mix for sending to specific receiver for specific goals, comparing the main aim of signaler of sending signals with the probable perception of the signal receiver, the probable reaction of signal receivers to signals based on their perception and the dimension of reactions(Debruyne et al.,2002).
Vinyas Harish, Thomas G Samson, Lori Diemert
et al.
Cross-sector partnerships are vital for maintaining resilient health systems; however, few studies have sought to empirically assess the barriers and enablers of effective and responsible partnerships during public health emergencies. Through a qualitative, multiple case study, we analyzed 210 documents and conducted 26 interviews with stakeholders in three real-world partnerships between Canadian health organizations and private technology startups during the COVID-19 pandemic. The three partnerships involved: 1) deploying a virtual care platform to care for COVID-19 patients at one hospital, 2) deploying a secure messaging platform for physicians at another hospital, and 3) using data science to support a public health organization. Our results demonstrate that a public health emergency created time and resource pressures throughout a partnership. Given these constraints, early and sustained alignment on the core problem was critical for success. Moreover, governance processes designed for normal operations, such as procurement, were triaged and streamlined. Social learning, or the process of learning from observing others, offset some time and resource pressures. Social learning took many forms ranging from informal conversations between individuals at peer organisations (e.g., hospital chief information officers) to standing meetings at the local university's city-wide COVID-19 response table. We also found that startups' flexibility and understanding of the local context enabled them to play a highly valuable role in emergency response. However, pandemic fueled "hypergrowth" created risks for startups, such as introducing opportunities for deviation away from their core value proposition. Finally, we found each partnership navigated intense workloads, burnout, and personnel turnover through the pandemic. Strong partnerships required healthy, motivated teams. Visibility into and engagement in partnership governance, belief in partnership impact, and strong emotional intelligence in managers promoted team well-being. Taken together, these findings can help to bridge the theory-to-practice gap and guide effective cross-sector partnerships during public health emergencies.
Computer applications to medicine. Medical informatics