Learning Literature Research Articles

Strengthening Language and Literature Competencies of High School Student

The scientific approach and contextual teaching which is widely used in science learning has not been widely used in language learning, especially in the 2016 Indonesian Language and Literature Curriculum. This study aims to analyze the relationship between the application of a scientific approach and contextual teaching in the application of the 2016 Indonesian language and literature curriculum with the achievement of learning Indonesian language and literature for class X SMA students. This study uses a type and quantitative research approach with surveys with correlational techniques. The research subjects were students of senior high school. Samples were taken randomly in class X. Data collection method uses in this study are observation, documentation, and interview. The research instruments were interviews and objective tests and essays which had been tried out before being used. Data analysis techniques used are product moment correlation, partial correlation, and double regression. From the results of the analysis, it can be concluded that there is a significant relationship between the use of SA and CTL together with achievement in learning Indonesian Language and Literature (score above 78). The implication of this research is the importance of using SA and CTL in private schools that have not used these two approaches in learning.

Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on 3 stochastic non-linear reinforcement learning tasks.

A Bayesian record linkage model incorporating relational data

AbstractIn this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter, Facebook, and YouTube.

The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies

Given an unsupervised outlier detection task, how should one select i) a detection algorithm, and ii) associated hyperparameter values (jointly called a model)? E ective outlier model selection is essential as di erent algorithms may work well for varying detection tasks, and moreover their performance can be quite sensitive to the values of the hyperparameters (HPs). On the other hand, unsupervised model selection is notoriously difficult, in the absence of hold-out validation data with ground-truth labels. Therefore, the problem is vastly understudied in the outlier mining literature. There exists a body of work that propose internal model evaluation strate- gies for selecting a model. These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models. In this paper, we rst survey internal model evaluation strategies including both those proposed speci cally for outlier detection, as well as those that can be adapted from the unsupervised deep representation learning literature. Then, we investigate their e ectiveness empirically in comparison to simple baselines such as random selection and the popular state-of-the-art detector Isolation Forest (iForest) with default HPs. To this end, we set up (and open-source) a large testbed with 39 detection tasks and 297 candidate models comprised of 8 different detectors and various HP con gurations. We evaluate internal strategies from 7 di erent families on their ability to discriminate between models w.r.t. detection performance, without using any labels. Our study reports a striking nding, that none of the existing and adapted strategies would be practically useful: stand-alone ones are not signi cantly di erent from random, and consensus-based ones do not outperform iForest (w/ default HPs) while being more expensive (as all candidate models need to be trained for evaluation). Our survey stresses the importance of and the standing need for e ective unsupervised outlier model selection, and acts as a call for future work on the problem.

Fairness and Risk: An Ethical Argument for a Group Fairness Definition Insurers Can Use

Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is well known from many application fields that such biases occur frequently and naturally when prediction models are applied to people unless special efforts are made to avoid them. Insurance is no exception. In this paper, we provide a thorough analysis of algorithmic fairness in the case of insurance premiums. We ask what “fairness” might mean in this context and how the fairness of a premium system can be measured. For this, we apply the established fairness frameworks of the fair machine learning literature to the case of insurance premiums and show which of the existing fairness criteria can be applied to assess the fairness of insurance premiums. We argue that two of the often-discussed group fairness criteria, independence (also called statistical parity or demographic parity) and separation (also known as equalized odds), are not normatively appropriate for insurance premiums. Instead, we propose the sufficiency criterion (also known as well-calibration) as a morally defensible alternative that allows us to test for systematic biases in premiums towards certain groups based on the risk they bring to the pool. In addition, we clarify the connection between group fairness and different degrees of personalization. Our findings enable insurers to assess the fairness properties of their risk models, helping them avoid reputation damage resulting from potentially unfair and discriminatory premium systems.

MINORITY RIGHTS FIGHTER IN THE LITERATURE: RESEARCH IN THE NOVELS SEJUTA DOA UNTUK GUS DUR BY DAMIEN DEMATRA

Discrimination against the rights of minorities must be avoided by the majority group by prioritizing the views, attitudes and behaviors that respect, accept and acknowledge all differences. The focus of this study describes the figure of Abdurahman Wahid who defended and fought for the rights of minorities contained in the novels Sejuta Doa untuk Gus Dur by Damien Dematra.This research is a type of qualitative research, with content analysis data analysis techniques. The research data are sourced from the novel A Million Prayers for Gus Dur by Damien Dematra in the form of Abdurahman Wahid's defense or struggle against minorities. Data collection techniques using the technique of reading and taking notes. Data validation uses theory triangulation. The findings show Abdurahman Wahid's defense and struggle against minority rights. Gus Dur's struggle in viewing minority rights can be seen from the thoughts, attitudes and actions of Gus Dur who care for minorities who experience discrimination and violence. Gus Dur's consistency in defense for minority rights has implications for harmony between religious communities.The results of this study can be useful for society as a reflection of living in harmony, which can respect a difference. In addition, for lecturers, teachers, and student the results of this study are useful as an alternative to learning literature at schools and universities from a multicultural value perspective. This study examines Gus Dur's attitudes, views and behavior in defense for minority rights. Respect for minority rights is a form of living in harmony. Gus Dur's struggle and consistency in defending the rights of minorities should be practiced by every citizen by respecting, accepting and acknowledging all the differences. Keywords: discrimination, minority, struggle, Gus Dur

Pendampingan Menulis Cerita Fabel Berbasis Kearifan Budaya Lamaholot di SMPS Ratu Damai, Flores Timur

Assistance in writing fairy tales for students at school is very important. Fairy tales are stories from the animal world that aim to entertain as well as a medium of moral education. Fable writing skills can only be achieved through directed, guided, and continuous practice in learning literature where students can translate their ideas, creative thoughts, and feelings into written language. This assistance aims to explain the implementation of the Lamaholot culture-based fable story writing activity with image media to the students of Ratu Damai class. The method used is the KKN method which directly assists students in writing fables using media images with implementation stages, including (1) the pre-implementation stage, (2) the analysis stage, (3) the training stage, (4) the mentoring stage, and (5) evaluation stage. This stage of activity is adjusted to the structure of the fable story which includes orientation, complication, resolution, and coda. Through fable writing assistance using image media, students can write fables which can be seen from the work in the form of fables written with imagination, high creativity, and interesting titles and moral messages conveyed regarding Lamaholot's cultural wisdom towards the marine environment. This guided activity can monitor students' interest and creativity in writing fables based on a given theme.

PESAN PROFETIK CERPEN BERTEMA ‘AMPLOP KIAI’, SEBUAH KAJIAN SOSIOLOGIS DAN IMPLEMENTASINYA DALAM PEMBELAJARAN SASTRA DI SEKOLAH

AbstractThis study took four short stories as material objects, namely "Amplop susulan" by Teguh Effendi, (Effendi, 2017) "Kiai Amplop" written by Edy Yuswanto, (Yuswantoro, 2011) "Amplop ustadz manfaat" is a short story by Effendi Thahar, (Thahar, 2016) "Amplop-Amplop Abu-Abu" written by a kiai named "A. Mustofa Bisri" (Bisri, 2017). The research method used in this research is qualitative method. The stages carried out are research and take noted and then describing. The purpose of the research is to find the perception or response of the kiai or ustadz to the envelope he received, to find prophetic messages in the short stories studied, and their implementation in learning literature at school.

Organizational changes approaches to facilitate the management of Industry 4.0 transformation in manufacturing SMEs

PurposeThis paper aims to investigate the characteristics and dynamics of the organizational changes needed to facilitate the management of an Industry 4.0 transformation in manufacturing SMEs and propose an approach to manage them.Design/methodology/approachThis research focuses on a single manufacturing SME in North America, and data were collected using a research intervention method. Data were collected through observation and intervention within the SME over 27 months.FindingsThe research has shown that organizational changes are required in manufacturing SMEs to better manage their Industry 4.0 transformation projects.Research limitations/implicationsUsing the case study method limits the generalization of the results. The organizational changes observed, and their characteristics might be specific to the studied manufacturing. Although results could vary in different contexts, many manufacturing SMEs have similar characteristics to those observed in this study.Practical implicationsThis research provides preliminary evidence of an iterative organizational change management approach that manufacturing SMEs must adopt to facilitate the management of their digital transformation.Originality/valueThis research provides a better understanding of how a manufacturing SME can improve its capabilities to manage its digital transformation by introducing iterative organizational changes. From these results, a link to the organizational learning literature can be drawn and developed upon.

The future of the U.S. safety & health workforce: Opportunities for academic diversity, equity, and inclusion through a leak-proof career pathway

Introduction: In this commentary, we explore safety and health career pathways in the U.S. and discuss the future of work through the lens of diversity, equity, and inclusion. We address occupational safety and health inequities, including underrepresentation of racial and ethnic groups in academic disciplines, which impacts workers' safety and the future of work. We introduce the Accessibility, Sustainability, and Knowledge-Generation (ASK) framework to broaden minority participation in occupational safety and health (OSH) education. Method: The ASK framework integrates insights from learned experiences, research literature, accreditation knowledge, and community feedback to identify strategies for dismantling barriers to workforce diversity and promoting equitable participation and access to opportunities in OSH education. We conducted a US based survey to gain insights into the minority experience and the role of students, faculty, and academia in mitigating disparities and creating pathways of opportunities. Results: The survey results highlight the need for pedagogic improvements in OSH academic programs to attract more minorities. The appreciation of cultural competence across universities and changes to current systemic funding mechanisms are identified as crucial factors. We acknowledge that existing interventions have been less effective due to a lack of tangible actions. However, we emphasize that solutions exist, and meaningful support is required to drive change. Conclusions: The study underscores the importance of addressing occupational safety and health inequities and the underrepresentation of minority groups in OSH education. The ASK framework provides a comprehensive approach to broaden minority participation and promote diversity in the field. Pedagogic improvements, cultural competence, and changes to funding mechanisms are necessary steps to attract and retain underrepresented minorities in OSH academic programs. Practical Applications: The findings of this study have practical implications for institutions and stakeholders involved in occupational safety and health education. It emphasizes the need for proactive strategies to attract and support underrepresented minority students and faculty. Promoting cultural competence and making changes to funding mechanisms can contribute to creating more equitable and inclusive pathways for diverse groups in OSH education. Tangible actions and meaningful support are essential for driving change and achieving workforce diversity and equity.

A Survey of Deep Learning for Alzheimer’s Disease

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

Analisis Transitivitas Teks Takepan Banyuurung dan Relavansinya terhadap Pembelajaran Sastra

The problem studied in this study is the transitivity analysis of Banyuurung text. Based on the logic of this problem, it is also the problem in this study that describes the transitivity system and its relevance to learning literature in schools. The approach used in this study is descriptive qualitative and based on the theory of Systemic Functional Linguistics (LFS). The method of data collection is done through documentation of transcripts, studies, and notes as a technique. The data analysis was carried out using identification techniques and the results of data analysis used the description method, describing and describing the data that had been collected or existed. The research findings show that the transitivity system in Banyuurung text (mental process) is 60 %) and the process of being is 60%. In addition to the process of being, goals, actors, behavior, phenomena, sensing with a total of 20%.

Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques

In this paper, we propose and compare new methodologies for ranking the importance of variables in productive processes via an adaptation of OneClass Support Vector Machines. In particular, we adapt two methodologies inspired by the machine learning literature: one involving the random shuffling of values of a variable and another one using the objective value of the dual formulation of the model. Additionally, we motivate the use of these type of algorithms in the production context and compare their performance via a computational experiment. We observe that the methodology based on shuffling the values of a variable outperforms the methodology based on the dual formulation. We observe that the shuffling-based methodology correctly ranks the variables in 94% of the scenarios with one relevant input and one irrelevant input. Moreover, it correctly ranks each variable in at least 65% of replications of a scenario with three relevant inputs and one irrelevant input.

Entrepreneurial Learning Based on the Zone of Proximal Development

In this article, we establish a dialogue between the education and entrepreneurial learning literatures to explore entrepreneurship education and practice. Our aim is to understand how entrepreneurial learning unfolds, based on the concept of the Zone of Proximal Development (ZPD). We adopt a constructivist approach to explore entrepreneurial learning, grounded in Cultural-Historical Theory. A combination of narrative research, focus group, storytelling, and theatrical images comprises our methodological approach, which draws from 34 virtual meetings with 10 undergraduate students who are also startup founders in Brazil. Our results show that, in addition to knowledge that is systematized by the university, entrepreneurial learning at university encompasses social relationships and is rooted in the experiential heritage acquired by students before entering university. Significantly, we show that the ZPD is built through interactions with entrepreneurship educators, but also key actors (family members, professional acquaintaces, experienced entrepreneurs, peer student entrepreneurs). Moreover, we spotlight the importance of intentionally planning the activities of student entrepreneurs within the learning context.

BUILDING A CONCEPTUAL MODEL FOR INTERNAL AUDITING: THE PERSPECTIVE OF ABSORPTIVE CAPACITY THEORY

The purpose of this paper is to present a conceptual model for internal auditing based on absorptive capacity theory. Internal auditing effectiveness is influenced by an organization's capacity to identify, assimilate, and apply new knowledge, according to the model. It has been determined that absorptive capacity is positively associated with innovation, performance, and competitiveness, making it a central concept in the literature of organisational learning. This study contends that internal audit functions contribute significantly to an organization's absorptive capacity by providing valuable knowledge, expertise, and perspective on internal control systems, risk management, and governance. An organisation can identify and learn from internal and external knowledge sources, including best practises, regulations, and industry developments, if its internal audit function is proactive and provides value-added services. In addition, a well-aligned internal audit function with the strategic objectives and culture of the organisation can facilitate the assimilation and application of new knowledge. The model suggests that internal audit functions can be improved by employing a risk-based approach that focuses on the organization's most significant risks and opportunities. This strategy can assist internal auditors in identifying and prioritising areas where new knowledge is required and in delivering more targeted and pertinent services. In addition, the model proposes that internal audit functions can be improved by leveraging digital technologies, such as data analytics and information technology, which can assist internal auditors in accessing and processing large quantities of data and delivering more efficient and effective services. This paper emphasises the significance of absorptive capacity for internal auditing effectiveness and provides a conceptual model that can guide the development of proactive and value-added internal audit functions. Practitioners, researchers, and policymakers interested in enhancing the performance and competitiveness of organisations through internal auditing may find the proposed model valuable.

Spatiotemporal Sequence Prediction With Point Processes and Self-Organizing Decision Trees.

We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the nonstationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters.

Assessing the predictive capacity of machine learning models using patient-specific variables in determining in-hospital outcomes after THA

BackgroundMachine learning is a subset of artificial intelligence using algorithmic modeling to progressively learn and create predictive models. Clinical application of machine learning can aid physicians through identification of risk factors and implications of predicted patient outcomes. AimsThe aim of this study was to compare patient-specific and situation perioperative variables through optimized machine learning models to predict postoperative outcomes. MethodsData from 2016 to 2017 from the National Inpatient Sample was used to identify 177,442 discharges undergoing primary total hip arthroplasty, which were included in the training, testing, and validation of 10 machine learning models. 15 predictive variables consisting of 8 patient-specific and 7 situational specific variables were utilized to predict 3 outcome variables: length of stay, discharge, and mortality. The machine learning models were assessed in responsiveness via area under the curve and reliability. ResultsFor all outcomes, Linear Support Vector Machine had the highest responsiveness among all models when using all variables. When utilizing patient-specific variables only, responsiveness of the top 3 models ranged between 0.639 and 0.717 for length of stay, 0.703–0.786 for discharge disposition, and 0.887–0.952 for mortality. The top 3 models utilizing situational variables only produced responsiveness of 0.552–0.589 for length of stay, 0.543–0.574 for discharge disposition, and 0.469–0.536 for mortality. ConclusionsLinear Support Vector Machine was the most responsive machine learning model of the 10 algorithms trained, while decision list was most reliable. Responsiveness was observed to be consistently higher with patient-specific variables than situational variables, emphasizing the predictive capacity and value of patient-specific variables. The current practice in machine learning literature generally deploys a single model, it is suboptimal to develop optimized models for application into clinical practice. The limitation of other algorithms may prohibit potential more reliable and responsive models.Level of Evidence III.

Learn to survive crises: The role of firm resilience, innovation capabilities and environmental dynamism

Exogenous crises, while disruptive, may also present learning opportunities that could affect a firm's viability and performance. In this study, we examine how exogenous crises can constitute learning opportunities and assess their impact on firm survival. In particular, we investigate the role of learning in response to exogenous crises and how firm resilience, innovation capabilities, and environmental dynamism influence this relationship. Drawing from crisis management and organisational learning literature, we propose that these factors can bolster the connection between learning from crises and firm survival. To test our hypotheses, we conduct a nuanced analysis using both regression analysis and Fuzzy Set Qualitative Comparative Analysis (fsQCA) on data from 249 Italian manufacturing Small and Medium-sized Enterprises (SMEs). This approach allows us to simultaneously examine the impact of firm resilience, innovation capabilities, and environmental dynamism on the relationship between learning from crises and firm survival. Our findings offer theoretical and practical insights into the role of learning from crises in a firm's survival. They also highlight the importance of embracing learning opportunities in crisis situations and suggest that how firms deal with crises could be an opportunity to fine-tune their internal processes and thrive in the long run.

Improving Student Learning Outcomes through the Think Pair and Share Model in Learning English Language and Literature

The cooperative learning model of the Think Pair and Share (TPS) type is a "cooperative learning model that was first developed by Professor Frank Lyman and his colleagues at the University of Maryland in 1981. This research is a classroom action research. The research subjects were class XII MIPA 1 students of SMA Negeri Integrated Madani Palu Model, a total of 34 people consisting of 11 men and 23 women. While the research procedure consists of 4 stages, namely: planning, implementation, observation and reflection. Further data analysis is carried out through three stages, namely data reduction, data presentation, and data/conclusion verification. The results of the study show that the Think Pair and Share Model can improve learning outcomes in learning English language and literature for class XII MIPA 1 SMAN Integrated Madani Model Palu, Academic Year 2022/2023

Effects of a collaborative and gamified online learning methodology on class and test emotions

This study examines the influence of students’ individual attitude and social interactions on participation in collaborative and gamified online learning activities, as well as the influence of participating in those activities on students’ online class- and test-related emotions. Based on a sample of 301 first year Economics and Law university students and using the Partial Least Squares-Structural Equation Modelling approach, all the relationships among first-order and second-order constructs included in the model are validated. The results support all the hypotheses studied, confirming the positive relationship that both students’ individual attitude and social interactions have on participation in collaborative and gamified online learning activities. The results also show that participating in those activities is positively related with class- and test-related emotions. The main contribution of the study is the validation of the effect of collaborative and gamified online learning on university students’ emotional well-being through the analysis of their attitude and social interactions. Moreover, this is the first time in the specialised learning literature that students’ attitude is considered as a second-order construct operationalised by three factors: the perceived usefulness that this digital resource brings to the students, the entertainment that this digital resource brings to the students, and the predisposition to use this digital resource among all those available in online training. Our findings aim to shed light for educators when preparing and designing computer mediated and online teaching programs that seek to generate positive emotions as a motivation for students.