Knowledge In Learning Process Research Articles

Strategi Peningkatan Mutu Pembalajaran Pendidikan Agama Islam

The purpose of this study is to identify the following: (1) Strategies for Enhancing the Quality of Islamic Religious Education at the Qon Gresik Islamic Vocational School; and (2) Factors That May Help or Hinder the Improvement of Islamic Religious Education at the Qon Gresik Islamic Vocational School. It is intended that this research will help the Qon Gresik Islamic Vocational School provide higher-quality Islamic religious instruction. This study employs a descriptive qualitative methodology. Interviews, observations, and documentation are methods used in data collection. The researcher is the tool used in the research. Descriptive analysis techniques are used in data analysis, which is the preparation of data for processing, justification, and analysis that happens concurrently with data collection. According to the research findings, there are a number of ways to raise the standard of Islamic religious education instruction, including through methodologies, learning procedures, habituation, and extracurricular activities. According to the fundamental principles of learning that educators generally apply, students at the Qon Gresik Islamic Vocational School participate in the learning process with high enthusiasm in the general knowledge learning process, especially more knowledge of the Islamic religion. This approach helps the school improve the quality of learning in the field of Islamic studies. Numerous tactics are employed, including placing a strong emphasis on assignments, enhancing the caliber of human resources—teachers—through training and education, and building up the infrastructure that supports education.

A deeper knowledge tracking model integrating cognitive theory and learning behavior

Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior.

A Comparative Analysis of Discourse Knowledge Structures in Different Disciplines of Education in Colleges and Universities in the Context of Informatization

Abstract In this paper, we first construct the discourse knowledge structure of education in different disciplines so that the knowledge points in a certain cross-cutting area between different disciplines are finite sets called knowledge domains, and we use the network of the discourse field system to characterize the logical relations in the knowledge space. Secondly, by defining the elements of hypertext as a triadic structure, a specific chain of dependencies is constructed by combining the hypertext structure with the knowledge space. Finally, we conducted a comparative analysis of the knowledge structure of educational discourse in various disciplines. The results show that among 45 knowledge states, 15 attributes corresponding to the whole column have all 0 information values and the knowledge structure model based on the knowledge space can master the corresponding effective skills of students in different disciplines, which indicates that it realizes this learning process of knowledge learning from point to point, from concrete to abstract, and from part to the whole under different disciplines. This paper examines the use of discourse in various knowledge structures, which may provide insights into the teaching of English academic writing in China.

Navigating Disruptive Times: How Cross-Sector Partnerships in a Development Context Built Resilience During the COVID-19 Pandemic Outbreak

This article explores how cross-sector partnerships (CSPs) operating in a development context built resilience during the outbreak of the COVID-19 pandemic. Based on a qualitative analysis of eight partnerships operating in East-Africa, Central America, and Indonesia, I show how CSPs engaged in three practices of resilience building (i.e., forming unconventional alliances, mobilizing digital technologies, and building subnetworks), which allowed them to remain functional despite facing adversity. In addition to fostering their resilience, my findings show how engaging in these practices enabled the CSPs to develop new capabilities (i.e., improved abilities to engage with beneficiaries) that benefit them in the long run. Based on these insights, I advance our understanding of resilience building by unpacking this concept on a CSP level. Furthermore, by unfolding the relevance of incidental learning in a cross-sectoral setting, I widen our knowledge of learning processes in CSPs.

Research on Knowledge Learning of COVID-19 Video Viewers: Based on Cognitive Mediation Model.

During the COVID-19 epidemic, social media has become the main channel for people to learn information related to the epidemic, among which information in the form of videos has played a significant role in the prevention and control of COVID-19. However, few studies have analyzed the process of knowledge learning of individuals through watching COVID-19 videos. Therefore, to explore the process of COVID-19 video viewers' knowledge acquisition, this paper constructs a knowledge learning path model based on the cognitive mediation model and dual coding theory. A sample of 255 valid questionnaires was collected to validate this model. The results of this study show that an individual's perceived risk of COVID-19 affects their surveillance motivation positively, while surveillance motivation further stimulates the attention and elaboration about the information in COVID-19 videos. Among them, attention positively influences the elaboration about the information. Ultimately, both an individual's attention and elaboration positively influence the knowledge he or she acquires from the COVID-19 videos. This paper not only verifies the hypothesized relationships in the original cognitive mediation model, but also extends the model to the context of video knowledge learning. Analyzing the knowledge learning process of COVID-19 video viewers, this paper can provide suggestions for government propaganda departments and relevant media to improve public knowledge of COVID-19.

Effects of Instructor’s “Task Knowledge” and “Learning-Process Knowledge” on Educational Desirability Judgment

教授者は日々の教育実践の中で，具体的に扱う「問題」に加え，それに対する「学習者の取り組み」とも対峙する。本研究では，教授者の知識に関する既存の概念をふまえ「課題知識」と「学習過程知識」の2つを定義し，教授者が各知識を獲得したとき，教授者主導，学習者主体の教授学習法の望ましさ判断をどう変化させるかを検討した。実験の参加者は大学生とし，問題として「答えが複数ある文章題」を設定し，公立小学校5年生を学習者として想定してもらい，課題知識付与群（問題の解法と正解を提示：N＝147），学習過程知識付与群（学習者の解答例と出現率を提示：N＝136）で，事前事後デザインにより判断の変化を調べた。その結果，問題の難易度が学習者にとりどの程度かの判断の平均評定値は，事前から事後にかけ，2群で同じように上昇したが，教授学習法の望ましさ判断の平均評定値は，2群で異なる方向に変化した。課題知識付与群では，教授者主導を望ましいと判断する傾向が強くなり，学習者主体の傾向は弱くなった。一方，学習過程知識付与群では，教授者主導の傾向が弱くなり，学習者主体の傾向は強くなった。以上の結果は，教授者が課題知識，学習過程知識のいずれを基礎とするかにより，教授学習法の望ましさ判断を異なる方向に変化させることを示唆する。

Application of Exercise-Correlated Knowledge Proficiency Tracing Model based on Multiple Learning Objectives in English Teaching Curriculum Resources

The advanced invention of information technology overcomes the limitations of traditional teaching methods. The English-oriented online teaching system not only has rich course resources, but also provides technical support for students’ personalized learning. Therefore, using big data mining techniques may help to analyze students deeply and develop personalized study plans and content. Aiming at the deficiencies of online English teaching platform in the task of knowledge level diagnosis, an Exercise-correlated Knowledge Proficiency Tracing (EKPT) model that integrates multi-objective learning factors is proposed. The learning model maps students and practice information into meaningful knowledge space through practice and knowledge-related information, and quantifies students’ knowledge learning process through forgetting curve theory and learning curve theory. The experimental test results show that the EKPT model has the best performance in knowledge ability prediction and learning performance prediction. The mean absolute error (MAE) values of the EKPT model in the English1 dataset, English2 dataset, written auxiliary dataset and adaptive dataset are 0.32, 0.25, 0.39 and 0.34, respectively, with the smallest error. Therefore, the proposed EKPT model meets the requirements of English online learning. The research content has important reference value for the construction of online English teaching platform.

A multi-dimensional cognitive framework for cognitive manufacturing based on OAR model

With the production system shifting to a multi-variety and small-batch production mode, the production process faces more user requirements, changes, and uncertainties. To solve the above problems, it is necessary to obtain the status and trend changes information and provide information support for the optimization of decision-making and dynamic adjustment of the production system. However, the production system cognition faces the problems of state coupling, state dynamic transfer and transition, and multi-system interweaving, which makes the production system cognition face huge challenges. Combining technologies such as the Internet of Things, industrial big data, and artificial intelligence, cognitive manufacturing can realize dynamic cognition of the production process, support dynamic adjustment, and become a promising way to solve the dynamic changes and uncertainties of production systems. In addition, as a formal expression of information processing and knowledge learning process in cognitive informatics, the Object-Attribute-Relation (OAR) model can effectively guide the construction of the production process cognitive mechanism. Therefore, this paper proposes a multi-dimensional cognitive framework based on OAR model of the human cognitive world for the dynamic cognitive needs of production system. The framework carries out dynamic cognition from the three dimensions of the manufacturing unit, production situation, and production system, and builds the continuous cognitive abilities from the three dimensions of analysis, decision-making, and learning. By integrating intelligent algorithms in the fields of artificial intelligence, a computable digital twin model is constructed as a carrier to provide the cognitive enabling technologies and capabilities for the production system. Finally, the feasibility of the proposed framework is illustrated by the developed computational digital twin platform. The computable digital twin platform provides the production system with important cognitive capabilities such as states perception, trend prediction, optimization decision-making, and knowledge learning, to support the dynamic cognition and optimization decision-making of the production system, and lay a technical foundation for adaptive production and cognitive manufacturing.

Critical Discourse Analysis in the Classroom: A Critical Language Awareness on Early Children’s Critical Thinking

By using CDA as a tool in the classroom, this study visually and verbally analyses the children's growth. This is based on the understanding that learning is not content-oriented, but rather about the learning process of knowledge gained. Students who can think well will be successful in life. Therefore, this study aims to determine the app implementing critical discourse analysis on early children's development in the classroom. The method used is a qualitative approach with a literature review using sources from the internet, books, and related research outputs. Based on the study's findings, the critical thinking structure is explicit to make clear what is to be learned in order to facilitate writing skills, systematic to provide a consistent framework for focusing on both language and context, needs-based to ensure that course objectives and content are derived from students' needs, supportive to give teachers a central role in scaffolding students' learning and creativity, and empowering to give access to the pattern and possibilities.

The Profile of StudenThe Profile of Students’ Pedagogical Knowledge on the Implementation of Mentoring Programts’ Pedagogical Knowledge on the Implementation of Mentoring Program

This study aims at revealing the students’ pedagogical knowledge on the implementation of a mentoring program. There were 60 students of Biology Education Study Program from Muhammadiyah University of Bengkulu involved in this study. The students’ pedagogical knowledge is considered as students’ competency within five aspects (Knowledge of learning processes, Knowledge of individual characteristics, Knowledge of classroom assessment, Knowledge of teaching methods, Knowledge of classroom management). The instruments for data collection were essay tests which had been validated through the expert judgment. The scores representing the students' pedagogical knowledge were categorized into three categories (low, medium, and high). The collected data were analyzed using a descriptive quantitative approach. The results of the study reveal the students' competency in the ten indicators of pedagogical knowledge consisting of learning strategies (85%); feedback provision to students (76%); knowledge of cognitive development (78%); motivatingstudents (70%); formative and summative assessment (73%); lesson plan evaluation (78%); various teaching methods (73%); appropriate teaching method (83%); conditioning a conducive atmosphere (80%); classroom management (86%). Thus, it can be concluded that the students' pedagogical knowledge on the implementation of a mentoring program can be categorized as high and able to improve their experiences of teaching practices as prospective teachers.

Learning-Based Model for Evaluating the Impact of Neighborhood Design on Travel Behavior

This research developed an agent-based model that evaluates the impact of neighborhood design on travel behavior while accounting for habit formation, social interactions, various levels of information provision, and awareness of transport and land use system changes. The developed model employs a framework that integrates random utility maximization theory with reinforcement learning concepts to account for the bounded rationality and knowledge learning process. Moreover, the model utilizes the diffusions of innovations theory to simulate how agents propagate information across family members and co-workers. It also adds a time dimension to the modal shift process, which could be used to indicate the relative duration to reap the full benefits of proposed scenarios. The model was applied to a neighborhood in Kelowna, British Columbia, Canada, to assess the impact on travel behavior of the SMARTer growth principles. The results showed that retrofitting non-motorized networks has more impact on modal shift than retrofitting road networks. This implies that infrastructure investments related to providing more accessibility for non-motorized users may be more socially and sustainably profitable than investments in policies targeting auto users. In addition, the results revealed that land use policies led to higher modal shift to non-motorized modes compared to retrofitting the transportation network, which highlights the importance of integrating land use and transportation planning. Similarly, the results demonstrated that transportation demand management policies can provide a positive stimulus to commuters to maintain familiarity with active transportation (AT) modes, which led in the presented case study to an increase in AT modal share.

Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective

Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.

Progressive Mimic Learning: A new perspective to train lightweight CNN models

Knowledge distillation (KD) builds a lightweight Student Model (SM) and trains it to approximate a large Teacher Model (TM) by exploring knowledge learned by the TM, which shows effectiveness to train lightweight CNN models. However, training a small SM to achieve better performance remains a challenging problem. Recent researches on human learning behaviors show that both the knowledge from teachers and the knowledge learning processes of teachers are significant for students. Inspired by this characteristic, in this paper, we propose a new perspective, called Progressive Mimic Learning (PML), to train lightweight CNN models by mimicking the learning trajectory of the TM. In order to obtain a more powerful SM, the useful hints in the learning process of the TM are explored. To start with, the TM learning process is divided into multiple stages, and the last state of the TM in each stage is recorded as a landmark. The learning trajectory of the TM is composed of these landmarks. Then, a landmark loss is defined to constrain the SM to progressively mimic the learning process of the TM, by employing landmarks in the learning trajectory as a training hint of the SM. Several experiments are conducted on four benchmark data sets, CIFAR-10, CIFAR-100, Fashion-MNIST, and ImageNet-10, to investigate the performance of the PML. The results show that the PML can make SMs generate more accurate predictions than SMs trained by its counterparts.

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Knowledge is being imparted since ancient Gurukul times to the modern e-learning modes of education. Innovative pedagogical teaching approaches, advanced computer techniques, easy access to information and high-end institutional infrastructures have improved knowledge learning processes and learning environments immensely. In this article, certain findings which were realised while researching through the literature of “learning” in higher education are presented. The theories, researches & information evolved by different academicians, educationists and psychologists have provided a strong insight in to the overall learning process of education. The findings presented in the article are the conclusions realised during the process of literature study. The study was conducted in the search to understand “learning” in the U.G course of architecture education. The stage wise understandings in classifications of learning processes as propounded by theorists and further realised conclusions from those are mentioned herewith in the article.

Host-Country Institutions and Emerging Market Multinational Enterprises’ CSR Performance

Grounded in the institutional knowledge perspective, this study explores the mechanisms through which and the conditions under which exposure to high-quality host-country institutional environments can affect emerging market multinational firms’ corporate social responsibility (CSR) performance. In doing so, we propose a three- stage institutional knowledge learning process model, namely institutional knowledge acquisition, assimilation and exploitation. We argue that high quality of host-country institutions will enable the institutional knowledge acquisition process, but may hinder assimilation process, thus exhibiting an inverted U-shape relationship between the quality of host-country institutions and CSR performance of emerging market multinational enterprises. Moreover, we propose that institutional knowledge base of emerging market firms can positively moderate this curvilinear relationship by facilitating the institutional knowledge acquisition, assimilation, and exploitation process. The results of analyzing a longitudinal dataset of listed Chinese firms from 2009 to 2017 support our key arguments.

PENGARUH METODE DISCOVERY-INQUIRY TERHADAP PROFIL KETERAMPILAN BERPIKIR KRITIS SISWA PADA PEMBELAJARAN TOPIK KELARUTAN

The era of industrial revolution 4.0 contributes major challenges in 21st century of education in obtaining qualified students. Education process lead to the development of student skills and attitudes in addition to the knowledge learning process. Critical thinking skills (KBKr) is one of the 21st century learning skills that students should have and needs to be practiced during learning process in school. One of the learning method to practice KBKr is discovery-inquiry learning method. The purpose of this study is obtain the affect of discovery-inquiry learning method on student’s critical thinking skill achievement in topic of solubility. Method of research is pre-experimental and one-shot case study as the design of method research. Subjects were 41 students at one of the senior high school in Bandung. Instruments of this research are six questions of written test and interview guidelines. Highest achievement at the whole sub indicators is the high ability student’s group. Overall, the student’s critical thinking skill achievement on factors affecting solubility’s learning with discovery-inquiry is good.

Impact of creativity in nursing education

Nowadays, the world has changed from a production source to a services-driven force; there are new challenges and shifts towards meaningful learning, together with rapid technological, socio-economic and environmental changes which call for a specific educational approach that learners need to acquire meaningful knowledge while encouraging them to utilise their knowledge in learning process to develop lifelong learning skills in order to prepare them for a complex future. Creativity in nursing education is an essential competence in fostering nursing learners; as both creative and critical thinking skills are needed to identify issues and create critical solutions to improve learners’ cognitive attributes and reflection and engagement in learning by developing assessment strategies and inspiring internal motivation for lifelong learning during various teaching activities. Therefore, nursing programmes must prepare nurses who can successfully perform in an environment that demands innovative problem-solving. In this context, this paper reviews the impacts of creativity in nursing education.
 
 Keywords: Nursing education, creativity, creative thinking, strategies of creativity

Learning of Poetry Writing High School Students (SMA) : Exploration Study

Based on the results of the study, it was found that in learning to write poetry, Brebes Regency High School students found problems and needs of teachers and students, namely (1) the importance of developing interesting and fun writing poetry learning, (2) the importance of the knowledge learning process in writing poetry (3) There are still teachers who have not applied the right learning model in learning to write poetry, (4) the importance of using appropriate media in learning to write poetry, and (5) the importance of introducing students to multicultural learning in writing poetry. Based on the analysis of the needs of teachers and students the results needed are needed to develop learning to write poetry as follows: (1) easy to use by high school teachers wherever they are, (2) learning that is active, innovative, creative, effective and fun, and (3) improving social sense between friends and training to dare to write poetry with confidence.

EXPLORING THE COGNITIVE LEARNING PROCESS BY MEASURING COGNITIVE LOAD AND EMOTIONAL STATES

Cognitive load and emotional states may impact the cognitive learning process. Detection of reliable emotions and cognitive load would benefit the design of the emotional intelligent mobility system for visually impaired peoples (VIPs). Application of learning process using electroencephalography (EEG) offers novel and promising approaches to measure cognitive load and emotional states. EEG is used to identify the physiological index that can lead to detecting cognitive load and emotions which can help to explore the knowledge of learning processes. Basically, EEG is a record of ongoing electrical signals to represent the human brain activity due to external and internal stimuli. Therefore, in this study EEG signals are captured from participants with nine different degrees of sight loss people. EEG signals are then used to measure various cognitive load and emotional states to evaluate cognitive learning process for the VIPs. To support the argument of cognitive learning process, the complexity of the tasks in terms of cognitive load and emotional states are quantified considering diverse factors by extracting features from various well-established metrics such as permutation entropy, event related synchronization/desynchronization, arousal, and valence when VIPs are navigating unfamiliar indoor environments. A classification accuracy of door is 86.67% which is achieved by the proposed model. It has almost 10% of improvement compared to another state-of the-art method who have used same dataset. Moreover, we have achieved 10% and 1% more accuracy in the corridor and open space conditions compared to the existing method. Experimental results also demonstrated that learning process is significantly improved considering wide range of obstacles when they are navigating indoor environments.

SD Simulation Model of Knowledge Learning for Innovation Following Enterprises

In this paper, we established the system dynamics model of knowledge learning from leading enterprises to following enterprises in the case of knowledge spillover, and then studied the intrinsic mechanism of two different learning strategies “knowledge transfer and knowledge integration”. Research shows: (1) In the case of knowledge spillover, the knowledge stock, explicit knowledge, true tacit knowledge and pseudo-hidden knowledge of the following enterprise are all growing steadily in the process of learning. (2) Due to the continuous innovation investment of the innovative leading enterprise, the knowledge learning of the following enterprise has not reduced the knowledge gap between the two enterprises, instead, the gap has become larger. (3) The knowledge innovation output of the leading enterprise is rising continuously in the random fluctuations, while the knowledge level of the following enterprise is in a stable growth trend throughout the knowledge learning process, reflecting the low innovation risk of the followers.