Showing papers on "Knowledge acquisition published in 2021"

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Abstract: Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Abstract: For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general E xercise- E nhanced R ecurrent N eural N etwork (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism . Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable E xercise-aware K nowledge T racing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we conduct extensive experiments and evaluate both EERNN and EKT frameworks on a large-scale real-world data. The results in both general and cold-start scenarios clearly demonstrate the effectiveness of two frameworks in student performance prediction as well as the superior interpretability of EKT.

Enhancing green product and process innovation: Towards an integrative framework of knowledge acquisition and environmental investment

Abstract: Despite the increasing interest in green innovation literature, little is known on how and under what conditions firms' knowledge transfer activities affect green innovation. There is lack of research that on how particular organizational capabilities are seen more useful and how it influences on green innovation performance. To address this research gap, we examine a mediation model in which we explore whether a firm's knowledge acquisition capability and investment in environmental management mediate the impact of buyer‐driven knowledge transfer activities on green product innovation and green process innovation. On the basis of an analysis of a sample of 239 manufacturing firms, we find that buyer‐driven knowledge activities have a greater positive impact on green product innovation than green process innovation. Investment in environmental management fully mediates the relationship between buyer‐driven knowledge transfer activities and green process innovation, and knowledge acquisition capability partially mediates the relationship between buyer‐driven knowledge transfer activities and green product innovation. The current study provides evidence that internal competencies and the role of buyers in knowledge transfer are critical for explaining the green product innovation and green process innovation. Our results suggest that buyer involvement pushes firms to develop resource acquisition capability to enhance green product innovation. Our results also highlight the importance of investment in environmental management for overcoming the environmental challenges in the manufacturing firms.

A review of the evidence for training effectiveness with virtual reality technology

Abstract: Prior to adopting new technologies for training, evaluations must be executed to demonstrate their benefit. Specifically, the appeal of virtual reality has led to applications across domains. While many evaluations have been conducted on their effectiveness, there has yet been a review to summarize and categorize the evidence on training outcomes. To assess the benefits these new technologies may bring to the trainee, a review of the research on the training effectiveness with virtual reality (VR) technology that was conducted. The goal for this review was to take a domain-agnostic perspective to identify the knowledge, skills, and abilities (KSAs) that have been trained effectively or enhanced with the use of VR. This review searched the related literature within multiple databases and found publications that met the search criteria from 1992 to 2019. A discussion of previous VR training reviews is first presented, followed by an in-depth evaluation of the literature that met the inclusion criteria. Three distinct categories of KSAs were identified consistently: psychomotor performance, knowledge acquisition, and spatial ability. Recommendations to support achievement of training outcomes utilizing VR training systems are provided.

Ontology Learning for Systems Engineering Body of Knowledge

Abstract: Extant systems engineering standards are so fragmented that the conceptualization of a cohesive body of knowledge is not easy. The discrepancies between different standards lead to misunderstanding and misinterpretation, making communication between stakeholders increasingly difficult. Moreover, these standards remain document centric, whereas systems engineering is transforming from paper-based to a model-based discipline. This requires the use of advanced information exchange schema and digital artifacts to enhance interoperability. Ontologies have been advocated as a mechanism to address these problems, as they can support the model-based transition and formalize the domain knowledge. However, manually creating ontologies is a time-consuming, error-prone, and tedious process. Little has been known about how to automate the development and little work has been conducted for building systems engineering ontologies. Therefore, in this article, we propose an ontology learning methodology to extract a systems engineering ontology from the extant standards. This methodology employs natural language processing techniques to carry out the lexical and morphological analyses on the standard documents. From the learning process, important terminologies, synonyms, concepts, and relations constructing the systems engineering body of knowledge are automatically recognized and classified. A formal and sophisticated system engineering ontology is achieved, which can be used to harmonize the extant standards, unify the languages, and improve the interoperability of the model-based systems engineering approach.

A Novel Teaching Strategy Through Adaptive Learning Activities for Computer Programming

Abstract: Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students’ cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students’ knowledge level to fuzzy weights, and using rule-based decision making, delivers adequate learning activities regarding their kind and complexity degree. Background: Tutoring through adaptive learning activities can be a powerful tool to support learners in practical courses, like computer programming. However, published results from pertinent literature are not oriented to the adaptivity of the domain knowledge in terms of the volume, kind, and complexity of the learning activities delivered to students. There is scope for a lot of improvement to this direction. Intended Outcomes: Combining learning theories with adaptive tutoring enhances student-centered learning, promotes student engagement, and improves knowledge acquisition. Application Design: An adaptive tutoring system was developed for supporting undergraduate students in the C# programming language course for an academic semester. The system delivers adaptive learning activities to students’ cognitive skills using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of a RBT for designing the learning material. Findings: At the end of the academic semester, students’ data have been collected and a detailed evaluation was conducted. The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students’ learning outcomes.

Tracing Knowledge State with Individual Cognition and Acquisition Estimation

Abstract: Knowledge tracing, which dynamically estimates students' learning states by predicting their performance on answering questions, is an essential task in online education. One typical solution for knowledge tracing is based on Recurrent Neural Networks (RNNs), which represent students' knowledge states with the hidden states of RNNs. Such type of methods normally assumes that students have the same cognition level and knowledge acquisition sensitivity on the same question. Thus, they (i) predict students' responses by referring to their knowledge states and question representations, and (ii) update the knowledge states according to the question representations and students' responses. No explicit cognition level or knowledge acquisition sensitivity is considered in the above two processes. However, in real-world scenarios, students have different understandings on a question and have various knowledge acquisition after they finish the same question. In this paper, we propose a novel model called Individual Estimation Knowledge Tracing (IEKT), which estimates the students' cognition on the question before response prediction and assesses their knowledge acquisition sensitivity on the questions before updating the knowledge state. In the experiments, we compare IEKT with 11 knowledge tracing baselines on four benchmark datasets, and the results show IEKT achieves the state-of-the-art performance.

Understanding Indonesia millennia Ulama online knowledge acquisition and use in daily fatwa making habits

Abstract: Social media have influenced millennials of ulama or Muslim scholars in fatwa production. They access and use online knowledge to make daily fatwa according to community requests. However, limited is known about how the millennial ulama acquire and use the knowledge. We carried out an interpretive case study using a theoretical lens from knowledge creation and use in an information technology context and Islamic sociology. Data were gathered through in-depth interviews involving 36 millennial ulama as informants. The interviews were used to understand the respondents’ perspectives on knowledge acquisition and use in their daily fatwa making within a provincial level of Indonesia Ulama Council (MUI). The findings show that Indonesia’s millennial ulama have intensively acquired and trusted online Islamic knowledge. Also, they construct and use knowledge from online social network interaction in daily fatwa making. This study has implications for the simplicity of Islamic knowledge acquisition and fatwa making. Online knowledge acquisition might have reduced the roles of traditional Islamic education institutions and muftis. Therefore, further research should examine how new knowledge sources have caused this reduction.

Is M-learning acceptance influenced by knowledge acquisition and knowledge sharing in developing countries?

Abstract: Researchers have shown that knowledge acquisition and sharing have considerably influenced the acceptance of various technologies. However, there is a scarce of knowledge on how these two factors affect the acceptance of Mobile learning (M-learning). Thus, this research is believed to be one of the few attempts that aims to understand the impact of knowledge acquisition and knowledge sharing on M-learning acceptance through the extension of technology acceptance model (TAM) by these factors. The data were collected from 735 IT undergraduate students enrolled in two different academic institutions in two different developing countries, namely Malaysia and Oman, using questionnaire surveys. The partial least squares-structural equation modeling (PLS-SEM) is used to validate the extended theoretical model. The findings indicated that knowledge acquisition has a significant positive influence on perceived ease of use and perceived usefulness of M-learning in both samples. Moreover, the findings revealed that knowledge sharing has a significant positive impact on perceived usefulness with respect to the Omani sample, whereas this relation was not supported in terms of the Malaysian sample. Theoretical and practical implications, limitations, and future research directions are also discussed.

Virtual Reality-Based Training: Case Study in Mechatronics

Abstract: This paper introduces, explains and illustrates real-life application of virtual training tool for electrical engineering education. The tool gives users the opportunity to interact with and manipulate 3D models of authentic devices. The users have a possibility to compare structural differences between devices, assemble and disassemble the machines and test them under extreme conditions, all of which would not be possible while working with a real device. The 3D devices are fully operational allowing the users to interact with them on every level, including analysis of impact of supply conditions i.e. modify voltage and frequency of a particular motor and monitor changes in performance while still operating. The main goal of this research was to evaluate effectiveness and educational values of the proposed tool. Early studies and feedback from educators and students prove this tool to be a great assistance to process of education, facilitating knowledge acquisition and providing an innovative way to put theory into practice.

An interpretable knowledge-based decision support system and its applications in pregnancy diagnosis

Abstract: This paper aims to propose an interpretable knowledge-based decision support system (IKBDSS) that will assist physicians to predict the risk level of a disease. Our system enables to integrate both historical cases extracted from database and opinions provided by different experts in order to set up a medical knowledge base and provide relevant advises by inferring from the knowledge base. To present various experts’ opinions, the Multi-granularity Linguistic Term Sets (MLTS) model is used to address the ambiguity and intangibility of knowledge. Our work mainly focuses on knowledge acquisition, similarity degree calculation and consistency checking process. It is worth mentioning that a criterion weights calculation method is introduced to objectively obtain the weights based on knowledge from experts, rather than subjectively predefined. The developed system leads to a better performance in specificity, sensitivity and F 1 score compared to other methods in the literature. To conclude, our work contributes to: (1) The development of a medical decision support system to combine clinical records and domain knowledge to predict diagnosis. (2) The decision-making process ensures interpretability, which increases the reliability of our system in terms of being a decision supporter. (3) The criterion weights are calculated based on the professional knowledge presented in MLTS form, and this process improves the capacity of providing diagnostic recommendations.

Innovative performance of project teams: the role of organizational structure and knowledge-based dynamic capability

Abstract: This study aims to investigate the relationship between knowledge-based dynamic capability and organizational structure on team innovative performance in Brazilian industrial companies.,This study is based on data from a survey of 262 respondents from 65 companies in the Brazilian industrial sector with project teams and followed the partial least squares approach to model the structural equation that was used for data analysis.,The results of the study show that mechanical structures with a high degree of formalization and centralization have a negative impact on knowledge-based dynamic capability and integration has a positive relationship with dynamic capability. Moreover, the research shows that project team innovative performance is directly affected by knowledge generation and combination capability; however, knowledge acquisition/absorption does not interfere with project team innovative activity.,This study contributes to the managers of firms in the industrial sector by analyzing how the characteristics of organizational structure impact dynamic capability and project team innovative performance. The results of this study indicate that more mechanical structures have more difficulty in developing knowledge-based dynamic capability in the context of project teams.,This study advances the concept of knowledge-based dynamic capability from the firm level to the project team level. This study accesses a research gap that characterizes organizational structure as an antecedent of dynamic capability, analyzing the impact of organizational structure on the dimensions of dynamic capability and of the latter on project team innovative performance.

Knowledge and intellectual capital in internationalizing SMEs, case study in technology-based health companies

Abstract: International businesses play a significant role in the growth, innovation and survival of technology-based firms (TBFs). Creating new opportunities in international markets, using communication networks and confronting constraints on available resources have distinguished the internationalization of small and medium-sized enterprises (SMEs) and intensified the importance of knowledge acquisition and continuous learning. This study aimed to (1) configure (the types and sources of) and (2) measure the internationalization knowledge of TBFs, by focusing on a knowledge-intensive part of the health industry in an emerging country: Iranian advanced medical equipment companies.,For this purpose, a qualitative approach was adopted to interview the leaders of eight TBFs exporting medical equipment. Obtained data were investigated using content analysis.,According to the content analysis results for configuration, technological knowledge and market knowledge were mainly obtained through direct experience and vicarious learning; however, internationalization knowledge is not so established as the third major knowledge area to integrate technological with market knowledge in line with corporate strategies of an internationalizing firm. For measurement of knowledge assets of internationalizing firms as a prerequisite for continuous improvement, several intellectual capital indices were extracted, including human, structural and relational capital.,This research complements existing literature in internationalization knowledge configuration via deploying an "Intellectual capital" perspective. It could enhance efforts for improving the learning of internationalizing SMEs, especially in the developing countries.

Straight from the scientist's mouth - Plain language summaries promote laypeople’s comprehension and knowledge acquisition when reading about individual research findings in psychology

Abstract: Easily comprehensible summaries of scholarly articles that are provided alongside ‘ordinary’ scientific abstracts, so-called plain language summaries, can be a powerful tool for communicating research findings to a wider audience. Using an experimental within-person-design in a preregistered study (N = 166), we showed that the comprehensibility for laypeople was higher for plain language summaries compared to scientific abstracts in a psychological journal and also found that laypeople actually understood the corresponding information more correctly for plain language summaries. Moreover, in line with the easiness effect of science popularization, individuals perceived plain language summaries as more credible and were more confident about their ability to make a decision based on plain language summaries. If and under which circumstances this higher perceived credibility is justified, is discussed together with other practical implications and theoretical implications of our findings. In sum, our research further strengthens arguments for providing plain language summaries of psychological research findings by demonstrating that they actually work in practice.

Teaching strategies for linking knowledge acquisition and application in the architectural design studio

Abstract: PurposeIn the context of architecture education, design studio projects usually start with “research” on the design theme and the context, but often there is no strong link between this research and its application in the project and the resultant design product. This paper explores strategies which link knowledge acquisition and knowledge application in design studio teaching and learning.Design/methodology/approachThese strategies have been applied in several design studios and master’s theses and involve sixteen years of research by the author through observation, surveys and analysis of student work.FindingsThe results show that these strategies are not limited to the design studio, with more than half of them (eight out of fourteen) also applicable in theoretical subjects that sit outside the design studio unit and generate knowledge of relevance to studio projects. As such, the paper advocates for a multi-level approach involving the following: course design and curriculum development, teaching and learning pedagogies and organizational decisions regarding the deployment of staff as for collaborative team-based teaching.Research limitations/implicationsThe results also recognize the relevance of problem-based and project-based learning to the broader higher education context and its dependence on a collaborative approach.Originality/valueThis paper which synthesizes this work contributes to the literature on architecture pedagogy, specifically that related to the integration of theoretical and practical subjects.

Impact of social networking for advancing learners’ knowledge in E-learning environments

Abstract: Social networking has modernized digital education through the provision of novel functionalities, such as reacting, commenting, motivation or group formation. In the light of the new developments, this paper presents SNAKE (Social Networking for Advancing Knowledge in E-learning environment), which is an e-learning software incorporating social characteristics for the tutoring of computer programming. However, investigating the impact of e-learning software holding social characteristics is yet a quite under-researched area. To this end, an extensive exploration of SNAKE has been conducted which examined different factors affecting social networking-based learning. The population of this study included 200 undergraduate students of computer science. To analyze the disposable data, the structural equation modeling was utilized. Upon analysis and structural model validities, the experimentation led to an extended Technology Acceptance Model (TAM) utilized for estimating the impact of the various variables. In more detail, the research model consisted of the TAM core constructs and three external variables. Concluding, the study confirmed that the model adequately explained causal relationships between variables and presented direct and indirect significant impacts of them on SNAKE which can promote learners’ better academic performance and knowledge acquisition.

Personalized Education in the Artificial Intelligence Era: What to Expect Next

Abstract: The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses his/her weaknesses to ultimately meet his/her desired goal. This concept emerged several years ago and is being adopted by a rapidly growing number of educational institutions around the globe. In recent years, the rise of artificial intelligence (AI) and machine learning (ML), together with advances in big data analysis, has introduced novel perspectives that enhance personalized education in numerous ways. By taking advantage of AI/ML methods, the educational platform precisely acquires the student?s characteristics. This is done, in part, by observing past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, and connect appropriate learners by suggestion, accurate performance evaluation, and so forth. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing the diversity, removing the biases induced by data and algorithms, and so on. In this article, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.

Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling

Abstract: This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students' characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.

Enhancing Learning Performance and Motivation of Cultural Heritage Using Serious Games

Abstract: In recent years, more serious games are used in different learning areas to improve the process of knowledge acquisition and learning outcomes. This study discusses a game mode based on a jigsaw pu...

User Context Ontology for Adaptive Mobile-Phone Interfaces

Abstract: The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user’s context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protege on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge.

The creation and development of learning organizations: a review

Abstract: In this paper, using the antecedents, decisions and outcomes (ADO) framework, the factors/key performance indicators (KPIs) most relevant for creating or building a learning organization (LO) are identified. This study aims to contribute to the field of knowledge management (KM) in terms of introducing KPIs to foster a business organization with a continuous learning process, mechanisms of knowledge creation and memorization.,In total, 57 papers were selected for this systematic literature review (SLR) from Web of Science and Scopus covering the period 1985–2019.,The 12 most relevant KPIs are identified based on the literature survey conducted in the field of LO.,The managerial implications of this review paper will be an added advantage to the modern business organization worldwide that have adopted KM practices to foster knowledge management with information technology (IT) infrastructure. As IT infrastructure focuses on knowledge acquisition, dissemination and storage but the KPIs revealed through this review will help in transforming stored information as learning for the organization to improve its overall performance.,This review synthesizes prior studies and provides directions for future research.

Interactive Learning for Nurses Through Gamification.

Abstract: Objective The nursing professional development department purchased technology to create an innovative structure to engage nurses in educational offerings. The purpose of this study is to examine the effect of incorporating gamification on knowledge acquisition. Background Nursing administrators should support the use of gamification to improve the nurses' acquisition of knowledge. The clinical educators traditionally utilize lecture-based educational offerings preventing students from active participation. Gamification promotes learner engagement, critical thinking, and enjoyment. Methods A quasi-experimental study design with a 230-person convenience sample compared the knowledge acquisition of nurses before and after new hire orientation and basic electrocardiogram course. Technology purchased included iPad, GoPro, mobile apps, and websites. Results Incorporating gamification technology resulted in an increase in knowledge acquisition and engagement of learners. Conclusion Findings demonstrate gamification as an effective way to increase knowledge acquisition when compared with traditional methods.

A database of general knowledge question performance in older adults.

Abstract: General knowledge questions are used across a variety of research and clinical settings to measure cognitive processes such as metacognition, knowledge acquisition, retrieval processes, and intelligence. Existing norms only report performance in younger adults, rendering them of limited utility for cognitive aging research because of well-documented differences in semantic memory and knowledge as a function of age. Specifically, older adults typically outperform younger adults in tasks assessing retrieval of information from the knowledge base. Here we present older adult performance on 421 general knowledge questions across a range of difficulty levels. Cued recall data, including data on the phenomenology of retrieval failures, and multiple-choice data are available. These norms will allow researchers to identify questions that are not likely to be known by older adult participants to examine learning or acquisition processes, or to select questions within a range of marginal accessibility, for example. Comparisons with young adult data from prior databases confirms previous findings of greater knowledge in older adults and indicates there is preservation of knowledge from early adulthood into older adulthood.