Knowledge Space Research Articles

Technology for Filling Subject Ontologies of the Scientific Knowledge Space

Subject ontology in the context of this article is understood as a set of key concepts related to a certain field of science, with their semantic connections, supplemented by indexes of various classification systems describing this scientific field. Subject ontologies are a necessary component of each subspace that is part of the Unified digital space of scientific knowledge (DSSK). This article presents the results of research related to the construction of subject ontologies based on the created automated system for supporting terminological dictionaries and suggests a methodology for identifying new key terms in a particular field of science. The proposed methodology is based on the use of existing classification systems in conjunction with citation databases, such as Web of Science and Scopus for English–language publications and the Russian citation index for Russian-language publications. The methodology involves dividing the scientific field into a number of sections in accordance with the selected classification system, extracting from the CSB the core of articles related to each section, and from the articles - new author's keywords, which should constitute, in combination with the corresponding sections of classification systems, the basis of the subject ontologies of this scientific field.

Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Despite the development of technical means, the processes associated with the search for complete and accurate scientific information in a huge number of data sources are becoming more complicated. To reach a new level in the use of information processing technologies, first of all, a transition to a semantically meaningful representation is necessary for scientific knowledge extracted from information in a digital environment. In modern conditions, characterized by multidisciplinary research, the desired effect can be achieved by developing universal approaches to the storage and presentation of scientific knowledge. These approaches are reflected in the concept of the Common Digital Space of Scientific Knowledge. The paper presents an overview of the basic concepts in this area, which are used both to represent the elements of space and to provide access to them not only for humans, but also for software agents. Semantic libraries are considered as tools for constructing the knowledge space.

Castration, the archive, and the incel wiki

In recent years, the incel community has been the subject of much scholarly and public fascination due to its violent and spectacular misogyny, which has resulted in acts of mass violence. In this essay, I argue that the incel psyche can best be understood through a reading of the incel wiki, which was established after a large incel community on Reddit was banned. During the ban, all of the content was lost, and in an attempt to regain their power a group of users created a wiki as a collaborative knowledge repository and archive. I argue that this archive not only serves as a way of reclaiming power via the process of embracing the incels’ symbolic castration but reveals the social epistemology of the group, as well as their shared fantasies, by creating an imaginary order. As a collaborative knowledge space and archive, the wiki allows for the sublimation of incel desire.

Blockchain for social memory institutions: Functional value and possibilities

De-centralized storage and the closely-coupled related constancy of loaded data, immunity against hacker attacks, transaction history recording and complete transparency make the blockchain technology attractive not only for developing cryptocurrencies and economic transactions. The author reviews the world experience in applying the technology to various activities of social memory institutions and, in particular, individual programs based on the blockchain. The technology enables to provide control and insurance for pieces of art, to ensure copyright, to prevent illegal copying, to store digital copies and ori- ginal works created in the digital environment, to integrate resources using the key functionality of distributed databases. The possibilities and prospects for Russia are evaluated; the need for regulative foundation to define core functionality and legal liability of blockchain processes is emphasized. The possibility for using the technology for building the single knowledge space as the integrative model of digital museum, archival and library resources is analyzed.

Are We There Yet? Evaluating the Effectiveness of a Recurrent Neural Network-Based Stopping Algorithm for an Adaptive Assessment

Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations, RNN models can improve on the performance of these existing methods, in this work we take a different approach. Rather than directly comparing RNNs with other models, we are instead interested in the results when RNNs are combined with one of these existing models. In particular, we attempt to improve the performance of ALEKS (“A ssessment and LE arning in K nowledge S paces”), an adaptive learning and assessment system based on Knowledge Space Theory, through the use of RNN models. Using data from more than 1.4 million ALEKS assessments, we first build an RNN classifier that attempts to predict the final result of each assessment. After verifying the accuracy of these predictions, we develop our stopping algorithm, with the goal of improving the efficiency of the ALEKS assessment by reducing the total number of questions that are asked. Based on this stopping algorithm, we give a comprehensive analysis of the possible effects it would have on students. We show that the combination of an RNN with the ALEKS assessment can reduce the average assessment length by over 26%, while a high degree of accuracy is maintained.

Designing physical systems through a model-based synthesis approach. Example of a Li-ion battery for electrical vehicles

This paper presents an approach for the preliminary design of complex physical systems in the framework of systems engineering. This approach is centered on the activities and actors involved in the pre-design tasks. It focuses on the modeling of design problems with DEPS (DEsign Problem Specification), a recent formalism for specifying and modeling design problems in engineering. This design methodology completes simulation-based analysis approaches, which are mainly used today for the design of physical systems. In particular, our approach allows the synthesis of pre-design architectures, which analysis/simulation approaches cannot do. Starting from the textual specification of the requirements, the proposed approach builds a formal model of the design problem and solves it using Constraint Programming. Ideas and concepts related to this approach are discussed: the issue of reusability of problem models, the concepts of problem, knowledge and solutions spaces as well as the formal specification of requirements and everything else that distinguish a design problem model from a designed system model. An example of a Li-ion battery design for an electric vehicle acts as a practical use case for this article.

A Correct Response Model in knowledge structure theory

In knowledge space theory, the (latent) knowledge state of a student consists of the subset of test items that he masters in principle. Even at a given stage of apprenticeship, the student’s knowledge state may vary in a given collection of subsets. The collection of all possible states of all potential students forms a knowledge structure. In the modeling of student answer production, the probabilities governing the knowledge state are parameters. Moreover, for each item, two additional parameters capture the probabilities of careless errors and lucky guesses in the answers to the item. From all the latter parameters, the Correct Response Model (CRM) predicts the probability of a correct answer to any isolated item. From the same parameters, the Basic Local Independence Model (BLIM) predicts the probability of any possible pattern of correct responses. Here, a particular pattern records at a given time all the items to which a given student would provide correct answers. While general properties of the BLIM (such as identifiability of parameters) have been investigated, the simpler Correct Response Model still requires scrutiny. The present paper investigates the CRM as regards testability, identifiability and characterizability. It either provides explicit answers or points out serious difficulties garnered from various mathematical disciplines.

Skills first — An alternative approach to construct knowledge spaces

This paper combines two aspects in the construction of knowledge spaces. The first is to utilize a partial order of skills which belong to a given domain of knowledge to find items or problems which finally constitute a knowledge space or a learning space. Secondly the orders of skills of different (related) areas are coalesced by well known order operations to construct larger spaces.In the first part a so called basic set representation of an order is utilized to considerably simplify the construction of knowledge spaces.In the second part the techniques to “add” and “multiply” orders are applied to given orders resulting in new orders to which the set representation theory of the first part is applied. Properties of the building blocks are identified which can be transferred to the combination and yield properties of the final knowledge space which make their handling a bit more convenient. In particular, places which proved useful in the construction of the first part are identified when these order operations are utilized.

Generalizing quasi-ordinal knowledge spaces to polytomous items

Based on the celebrated Birkhoff theorem, Doignon and Falmagne (1985) establish a one-to-one correspondence between certain binary relations on a domain of (dichotomous) items and the quasi-ordinal knowledge spaces defined on it. It is shown that this construction can be generalized to apply to polytomous response formats, where response values are partially ordered so that they form a lattice. Polytomous knowledge states are defined for the case where these response scales are identical across items, as well as for item-specific response scales, a generalization which emerges quite naturally within the developed theoretical framework. Certain collections of polytomous knowledge states are characterized through precedence relations that form particular quasi-orders on an extended set of (virtual) items, including for each item several dichotomous instances representing all the possible response values. These kinds of polytomous knowledge structures can be built from data through applying to the extended item set procedures commonly used in knowledge structure theory. Confining consideration to finite sets of items and response values, the presented theory generalizes previous approaches in this direction. Its application is illustrated for data from the 2011 Trends in International Mathematics and Science Study (TIMSS).

African Scholarship and its Integration within the Global Knowledge Economy : Reflections from within the University of KwaZulu-Natal

Over the decades there have been continuous efforts to position African scholarship within the global knowledge economy. Against the backdrop of marginalisation and domination, the champions of African scholarship have been engaged with political, ideological, and philosophical agendas that attempt to legitimise the African knowledge enterprise. Using an anthropological lens, this paper presents the nuanced local/global dialectics related to the recognition of African scholarship. The paper is based on the reflections of a selected number of academics of African origin from the College of Humanities at the University of KwaZulu-Natal. It highlights their subjectivities towards the elusiveness of this concept and attempts to seek its relevance as a knowledge space within the global knowledge economy. Branded as the premier university of African scholarship, UKZN has embarked on vigorous curricular, pedagogical and research initiatives that seek to bring the meaningful transformation needed to position the institution as a truly African university. This meaningful transformation can only be achieved if knowledge production in on Africa is cognisant of an African worldview, encompassing African cosmological, ontological, and epistemological perspectives. Interviews with those who participated in this study revealed the need for African scholarship to go global. Although this was emphasised, the approach to it revealed three streams of scholars who are termed in this paper as the idealists, the moderates, and the extremists. Despite their varying subjectivities, the conclusion drawn from the interviews pays allegiance to Afrocentric paradigms as the only way African development can be achieved as it connects with other global knowledge systems.

A practical perspective on knowledge space theory: ALEKS and its data

The ALEKS (Assessment and LEarning in Knowledge Spaces) educational software system is an instantiation of knowledge space theory (KST) that has been used by millions of students in mathematics, chemistry, statistics and accounting. The software employs a probabilistic assessment based on KST for placement into an appropriate course or curriculum, a learning mode in which students are guided through course material according to a knowledge structure, and regularly spaced re-assessments which are also based on KST. In each of these aspects, the interactions of the student with the system are guided by the theory and by insights learned from student data. We present several relationships between theory and data for the ALEKS system. We begin by surveying the ALEKS system and examining some practical aspects of implementing KST on a large scale. We then study the effectiveness of the ALEKS assessment using both standard statistical measures and ones adapted to the KST context. Finally, we examine the learning process in ALEKS via statistics for the learning mode and its associated knowledge structures.

The School Library, a New Space for Knowledge

For fifteen years, educational actors (teachers, school librarians, academic advisors ...), among which the teacher librarian plays a major part in the Centers for Documentation and Information (CDI, school library ), have been teaching, evaluating, requiring students expectations on a supposed "cultural background" , commonly called “general culture” in French, supported by the educational institution and the social world. In a first part, we wish to define the question of an overall school culture and show how the school librarians have slowly claimed the development and support for a "Culture of information " for each student and, more broadly, each citizen. In a second step, we discuss the similarities, differences and links between “basic knowledge” and information culture. In which way are they similar, fundamentally different, what is specific and new in the “culture of information” for young people? In a third step, we frame the components of the culture of information, deeply rooted into culture and the digital developments of information. The culture of information is the field of curricula, educative actions and debates, revealing tensions within libraries, between school requirements and social expectations. In a fourth and final step, we will show and advocate for a dynamic conception of academic culture of information "in action", connected to the media and social events, in classrooms and school libraries, updating the weak links between general education and information culture.

Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.

The Impact of Traditional- and Adaptive-Responsive Online Homework Systems on Student Performance in General Chemistry: Analyzing Extra Credit Participation

The positive impact of online homework has been clearly shown in previous literature. Online homework has become especially important in these times as online education grows. The recent development of the adaptive-responsive homework systems (such as Assessment of Learning in Knowledge Spaces--ALEKS) calls for a comparison of the effectiveness of this new type of system to the traditional-responsive systems (such as Sapling Learning). This study quantitatively compares extra credit scores and final exams and final points between two types of homework systems in a general chemistry course. Results indicate that extra credit completion using ALEKS (the adaptive-responsive system) has a higher correlation with homework performance, final exam performance, and final grade compared to extra credit completion using Sapling (the traditional-responsive system). This relationship difference could be due to a difference in the nature of how extra credit is earned within each system. Keywords: Online Homework, Extra Credit, Chemistry Education, Quantitative Research DOI: 10.7176/JEP/12-6-02 Publication date: February 28 th 2021

The evolution of macrosystems biology

In an era of unprecedented human impacts on the planet, macrosystems biology (MSB) was developed to understand ecological patterns and processes within and across spatial and temporal scales. We used machine‐learning and qualitative literature review approaches to evaluate the thematic composition of MSB from articles published since the 2010 creation of the US National Science Foundation’s MSB Program. The machine‐learning analyses revealed that MSB articles studied scale and human components similarly to six ecology subdisciplines, indicating that MSB has deep ecological roots. A comparison with 84,841 ecological studies demonstrated that MSB has extended the knowledge space of ecology by examining large‐scale patterns and processes alongside anthropogenic factors, which was also confirmed by the qualitative literature review approach. Our analyses indicated that MSB emphasizes large scales, has deep roots in ecological disciplines, and may emerge as a new research frontier, but this last point has yet to be proven.

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

AI is remarkably successful and outperforms human experts in certain tasks, even in complex domains such as medicine. Humans on the other hand are experts at multi-modal thinking and can embed new inputs almost instantly into a conceptual knowledge space shaped by experience. In many fields the aim is to build systems capable of explaining themselves, engaging in interactive what-if questions. Such questions, called counterfactuals, are becoming important in the rising field of explainable AI (xAI). Our central hypothesis is that using conceptual knowledge as a guiding model of reality will help to train more explainable, more robust and less biased machine learning models, ideally able to learn from fewer data. One important aspect in the medical domain is that various modalities contribute to one single result. Our main question is “How can we construct a multi-modal feature representation space (spanning images, text, genomics data) using knowledge bases as an initial connector for the development of novel explanation interface techniques?”. In this paper we argue for using Graph Neural Networks as a method-of-choice, enabling information fusion for multi-modal causability (causability – not to confuse with causality – is the measurable extent to which an explanation to a human expert achieves a specified level of causal understanding). The aim of this paper is to motivate the international xAI community to further work into the fields of multi-modal embeddings and interactive explainability, to lay the foundations for effective future human–AI interfaces. We emphasize that Graph Neural Networks play a major role for multi-modal causability, since causal links between features can be defined directly using graph structures.

Integration of Components of the Ontological Knowledge Space to Assess the Impact of Energy on Quality of Life of the Population

Integration of Components of the Ontological Knowledge Space to Assess the Impact of Energy on Quality of Life of the Population

Screening of novel excipients for freeze-dried protein formulations

The typical excipients used as bulking agents and lyoprotectants for freeze-drying are usually limited to only a few selected substances, such as sucrose and mannitol. Considering the sheer diversity amongst proteins, it is doubtful that this limited choice should, in every case, provide the best possible option in order to achieve the most stable product. In this work, a screening of 12 proteins with 64 excipients was conducted in order to increase the knowledge space of potential excipients. Three critical quality attributes (CQAs) of the freeze-dried products, namely the solid state, the cake appearance and the protein integrity based on changes in tryptophan fluorescence were investigated by high throughput X-ray powder diffraction, image analysis and intrinsic fluorescence spectroscopy, respectively. It was found, that in some cases the excipient had a dominating influence on the CQAs, whilst in other cases the CQAs were primarily protein dependent, or that the CQAs were dependent on the combination of both. In the course of this investigation, a general view of potentially relevant excipients, and their interplay with various proteins, was obtained, thereby furthermore paving the way for the use of novel freeze-drying excipients.

Zeroing in on the best early-course metrics to identify at-risk students in general chemistry: an adaptive learning pre-assessment vs. traditional diagnostic exam

ABSTRACT General chemistry are key gateway courses for many Science, Technology, Engineering, and Mathematics (STEM) majors. To improve student outcomes, it is critical both to identify at-risk students and implement evidence-based instructional strategies and interventions. These remain active areas of investigation, with recent literature showing that cognitive, affective and demographic characteristics all contribute as risk factors. Here we report an assessment of demographic characteristics and early-course cognitive measures in predicting student outcomes in first-term general chemistry (GC1), comparing an adaptive learning pre-assessment using ALEKS (Assessment and LEarning in Knowledge Spaces) with a traditional diagnostic exam. Student performance was evaluated across three sections (345 students) in a quasi-experimental pre-test/post-test design using a common ACS final exam. We find that ∼57% of variance in ACS final exam scores and ∼60% of variance in final course grades is related to three predictors: initial ALEKS knowledge check, diagnostic exam score, and predicted first year QPA, an internal metric based upon high school GPA, ACT/SAT scores, and college of admission. While no single metric could identify even one-half of students scoring lowest in the course (i.e. in the bottom 15%, consistent with the percentage of D’s, F’s and withdrawals in our sections), in combination ∼ 80% were identified.

Developing a Framework for Public Involvement in Mathematical and Economic Modelling: Bringing New Dynamism to Vaccination Policy Recommendations.

ObjectivesThe Mathematical and Economic Modelling for Vaccination and Immunisation Evaluation (MEMVIE) programme aimed to explore, capture and support the potential contribution of the public to mathematical and economic modelling, in order to identify the values that underpin public involvement (PI) in modelling and co-produce a framework that identifies the nature and type of PI in modelling and supports its implementation.MethodsWe established a PI Reference Group, who worked collaboratively with the academic contributors to create a deliberative knowledge space, which valued different forms of knowledge, expertise and evidence. Together, we explored the key steps of mathematical and economic methods in 21 meetings during 2015–2020. These deliberations generated rich discussion, through which we identified potential points of public contribution and the values that underpin PI in modelling. We iteratively developed a framework to guide future practice of PI in modelling.ResultsWe present the MEMVIE Public Involvement Framework in two forms: a short form to summarise key elements, and a long form framework to provide a detailed description of each potential type of public contribution at each stage of the modelling process. At a macro level, the public can contribute to reviewing context, reviewing relevance, assessing data and justifying model choice, troubleshooting, and interpreting and reviewing outcomes and decision making. The underpinning values that drive involvement include the public contributing to the validity of the model, potentially enhancing its relevance, utility and transparency through diverse inputs, and enhancing the credibility, consistency and continuous development through scrutiny, in addition to contextualising the model within a wider societal view.Discussion and ConclusionPI in modelling is in its infancy. The MEMVIE Framework is the first attempt to identify potential points of collaborative public contribution to modelling, but it requires further evaluation and refinement that we are undertaking in a subsequent study.Supplementary InformationThe online version of this article (10.1007/s40271-020-00476-x) contains supplementary material, which is available to authorized users.