Learning Literature Research Articles

KONTRIBUSI PLATFORM SIBER SASTRA DALAM PEMBELAJARAN SASTRA UNTUK GENERASI NATIVE

This research aims to describe the contributions and obstacles faced during the use of cyber literature in learning literature for the native generation. This research uses the literature review method with secondary data as its research object. Secondary data is obtained from literature considered relevant to the topic through searches on several platforms such as google scholar, sinta, scopus, and researcg gate. In addition, this research also limits the data source within a five-year period from 2019 to 2024. The steps used in data analysis include identification, categorisation, thematic analysis, synthesis, and conclusion drawing. The results of the study show that the literary cyber platformis a platformthat can accommodate learning, especially in the field of literature. Literary cyber platforms are effective in increasing students' learning motivation. In addition, the literary cyber platformalso provides a place for students not only to learn to read and write, but can also be used as a medium for analysing literary texts. The analysis of literary texts is realised through the comment section provided on each platformwhere writers and readers can engage in meaningful communication. This communication is a form of analysis of the literary text presented.

Contemporary American Literature in Distance Learning: Creating Reading Motivation and Student Engagement

AbstractThe effectiveness of distance learning primarily depends on the motivation and engagement of students. The aim of this article is to determine whether the developed online course enhances students' motivation to read and their engagement with contemporary American literature. The methodology is based on an experimental design. A mixed‐methods approach was employed for data analysis, combining statistical data analysis (t‐test) with qualitative survey methods (pre‐ and post‐testing). The study was conducted with a sample of 150 Chinese students from two universities (Leshan Normal University and Wuhan Qingchuan University), who took the “Contemporary American Literature” course from September to December 2022. The pre‐and post‐testing data indicate significant improvements across most parameters. For instance, reading efficiency, students' confidence in their reading abilities, and their potential to succeed in reading tasks increased by 21.4%; the “Challenge” dimension showed a notable growth of 17.2%; curiosity rose by 16.7%; and engagement in reading also increased by 18.5%. This demonstrates that students developed a greater interest in stories, particularly those related to fantasy, mystery, and adventure, and formed a deeper connection with characters and narratives. Most notably, avoidance of reading tasks decreased by 16.7%, which is a positive outcome as it indicates a reduced likelihood of students avoiding reading assignments. Given the large sample size, the critical t‐value is approximately 1.96. Since all t‐values exceed the critical value of 1.96, the null hypothesis was rejected in favor of the alternative hypothesis for each dimension. This indicates that the course had a statistically significant impact on the students' motivation and engagement in reading. The practical significance of these results underscores the potential of contemporary American literature courses, particularly those utilizing interactive methods in distance learning.

Effectiveness of remote teaching during covid-19 pandemic in a small business program

Purpose The paper aims to compare remote teaching effectiveness with face-to-face classroom teaching. This study seeks to investigate the influence of remote teaching on student academic learning during the pandemic compared to prepandemic, utilizing self-reported data from students. Design/methodology/approach The analysis is based on information extracted from the Student Course Evaluation. The study defines Spring 2019 and Fall 2019 semesters as prepandemic and labels them as the comparison base period. Spring 2020, Fall 2020 and Spring 2021 semesters are considered “during the pandemic” for comparison. Findings Upon comparing the average scores, the study findings indicate that students experienced a reduction in learning during the pandemic in contrast to the prepandemic period. Notably, a substantial decline in student academic learning was observed in the Spring 2020, followed by the Fall 2020 semesters. However, with the passage of time, the adverse impact of remote teaching on student learning showed a gradual decrease. In the Spring 2021 semester, students’ self-reports on learning in their business courses returned to prepandemic levels. Research limitations/implications The study has relied on secondary data sources and student self-reporting. Future research should aim to use more controlled data to mitigate the limitations associated with secondary data. Practical implications The study suggests that remote teaching could be as effective as face-to-face classroom teaching, and administrators of universities and colleges may consider it a viable option in the future. Originality/value This paper adds an important (probably first) empirical study of remote teaching effectiveness to the field of online learning literature.

Explore and experience London: Learning literature and culture through immersive virtual reality in the EFL classroom

Explore and experience London: Learning literature and culture through immersive virtual reality in the EFL classroom

Literature as a Cross Cultural Communication Media by Andrea Hirata’s Edensor Novel in BIPA Learning

Abstract: Literature is considered as a medium of cross-cultural communication that can lead to the existence of a culture in a new environment. Literature used as a medium of cross-cultural communication can be a tool for BIPA learning. The purpose of this study is to describe the existence of cross-cultural communication through literature in BIPA learning. The data of this research are in the form of depictions of events experienced by characters in literature. The data obtained were then analyzed by interpreting, described and then analyzed. The analysis was carried out in an analytical descriptive way through the reading of literary works. The data collection technique used is a literature study by collecting qualitative data from previous studies and relevant theories in accordance with the theme of the study. The results of this study prove that Andrea Hirata's Edensor novel represents persistence with culture and as a medium of cross-cultural communication in BIPA learning. Based on the results of the study, it can be concluded that the cross-culture described in Edensor's novel is due to the persistence of an indigenous child's dream in a new environment. Keywords: literature, communication media, cross culture, novel, edensor.

What is informal workplace learning: a qualitative meta-synthesis

Purpose The purpose of this study was to provide a model for informal workplace learning. Despite the prevalence and recognized importance of informal learning in the workplace, scattered literature and different perspectives have caused a lack of shared understanding about its nature, and little research effort has been made in this direction so far. Design/methodology/approach This paper identified the most significant components of informal workplace learning using the qualitative meta-synthesis method. For this purpose, after searching sources from different databases and screening them, 17 sources were selected due to compatibility with the research criteria. The texts extracted from the sources were analyzed using a thematic analysis method and synthesized using an aggregation approach. Finally, a themes network was created as a model with 8 main components and 11 sub-components. Findings The results indicated that eight components were considered significant to informal workplace learning, including learning stimulus, awareness and intent to learn, action or experience, critical reflection on action, facilitators, collective learning, self-directed and context. Originality/value This meta-synthesis contributes to the coherence and integration of the informal workplace learning literature. The obtained conceptual model, in addition to the general components of informal workplace learning, provides an explanation of the characteristics of each and the relationships between its components to achieve a complete understanding of the nature of informal workplace learning. Since no study has been done so far to comprehensively identify the most significant components of informal workplace learning, it is not possible to refer to their results in comparison with the results of this study.

Reducing the tide of single-use plastic pollution: how the EU’s Single-Use Plastic Directive drives (and fails to drive) private company reflexivity

ABSTRACT Single-use plastics (SUPs) are having catastrophic effects on marine environments. Establishing effective reduction and circularity strategies is complex and necessitates a legal approach that fosters reflexivity and learning by regulated companies, especially those that produce and sell SUPs. Reflexive environmental law (REL) offers a promising starting point to identify drivers and techniques in law that foster reflexivity, yet fails to consider how to define and identify reflexive responses by regulatees. Using reflexive governance and social learning literature, this article identifies four responses: negative, single-loop adaptive, double-loop reflexive and triple-loop reflexive. The article uses an explorative case study of the EU’s Single-Use Plastics Directive (SUPD), and its transposition by France and Germany, to analyse how the SUPD fosters company reflexivity through techniques building autonomy, accountability and responsiveness and adjustability. Through, among others, interviews with companies that must comply with the SUPD, this article reveals that single-loop and double-loop responses are the most prominent. However, reflexivity is shown to be inhibited by institutional, organisational and market factors. Better understanding of the dynamics between reflexive drivers within law(s) and the institutional, organisational and market characteristics of regulated actors is needed to design more effective regulations to transform to a sustainable circular plastics economy.

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology.

Fast-tracking within formal sport coach education: the perspectives of British orienteering coaches and coach developers

ABSTRACT Sport coaching is embedded with ego and sociocentric beliefs which assumes that embodied experience as an elite athlete is necessary to become an effective coaching practitioner, at the expense of knowledge obtained via formal professional development opportunities. Problematically, national governing bodies have legitimised these beliefs by actively promoting the process of fast-tracking, which entitles elite athletes to accelerated progression through formal coach education courses, despite the potential issues this may cause. Consequently, the aim of this research was to understand and explore coaches’ and coach developers’ perspectives towards fast-tracking within British Orienteering’s formal coach education provision. Multiple qualitative data collection methods were employed, including virtual dyadic interviews (n = 2) and focus groups (n = 2) with coaches (n = 8) and coach developers (n = 3), in addition to the analysis of documents related to formal coach education courses. A phronetic iterative approach to data analysis was adopted, with three themes constructed: (1) knowing what good looks like: privileging practitioner experience and endorsing fast-track routes; (2) obtaining respect and coaches’ preference for fast-tracking; and (3) a flawed coach education system: creating a cottage industry and sympathising with coaches. The findings are critically discussed and situated within the specific sport coaching and broader professional learning literature.

Problem characteristics of problem-based learning in hospitality and tourism education

ABSTRACT The aim of this qualitative study is to examine problem characteristics that facilitate the achievement of problem-based learning outcomes in hospitality and tourism education. Rooted in constructivist grounded theory, 64 in-depth interviews were held with hospitality and tourism postgraduates who had attended a marketing course including problem-based learning activities offered at a comprehensive research university. A theoretical framework comprising six problem-level attributes (measurable, ethically sound, applicable to the real world, innovative, theoretically sound, stakeholder-diversified) and three learner-level attributes (peer discussion, teacher discussion, and sharing) was proposed to support problem design in problem-based learning. These findings theoretically expand the problem-based learning literature into hospitality and tourism education. The results provide administrators and educators with a basis for designing problem-based learning activities in hospitality and tourism.

Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making

In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provides transportation assistance to low-income people with upcoming court dates. Following this literature, one might allocate rides to those with the highest estimated treatment effect per dollar while constraining spending to be equal across race groups. That approach, however, ignores the downstream consequences of such constraints and, as a result, can induce unexpected harm. For instance, if one demographic group lives farther from court, enforcing equal spending would necessarily mean fewer total rides provided and potentially more people penalized for missing court. Here we present an alternative framework for designing equitable algorithms that foregrounds the consequences of decisions. In our approach, one first elicits stakeholder preferences over the space of possible decisions and the resulting outcomes—such as preferences for balancing spending parity against court appearance rates. We then optimize over the space of decision policies, making trade-offs in a way that maximizes the elicited utility. To do so, we develop an algorithm for efficiently learning these optimal policies from data for a large family of expressive utility functions. In particular, we use a contextual bandit algorithm to explore the space of policies while solving a convex optimization problem at each step to estimate the best policy based on the available information. This consequentialist paradigm facilitates a more holistic approach to equitable decision making. This paper has been This paper was accepted by Catherine Tucker, Special Issue on The Human-Algorithm Connection. Funding: This work was supported by Stanford Impact Labs [Start-Up Impact Labs/GEJBU], the Stanford Institute for Human-Centered Artificial Intelligence, and Harvard Data Science Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00345 .

Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis

This article develops a method to automatically discover and quantify human-interpretable visual characteristics directly from product image data. The method is generative and can create new visual designs spanning the space of visual characteristics. It builds on disentanglement methods in deep learning using variational autoencoders, which aim to discover underlying statistically independent and interpretable visual characteristics of an object. The impossibility theorem in the deep learning literature indicates that supervision with ground truth characteristics would be required to obtain unique disentangled representations. However, these are typically unknown in real-world applications, and are in fact exactly the characteristics that need to be discovered. Extant machine learning methods are unsuitable since they require ground truth labels for each visual characteristic. In contrast, this method postulates the use of readily available product characteristics (such as brand and price) as proxy supervisory signals to enable disentanglement. This method discovers and quantifies human-interpretable and statistically independent characteristics without any specific domain knowledge on the product category. It is applied to a dataset of watches to automatically discover interpretable visual product characteristics, obtain consumer preferences over visual designs, and generate new ideal point designs targeted to specific consumer segments.

How to cross the line: design principles for interdisciplinary education

Background Interdisciplinary learning is gaining popularity in higher education worldwide. Currently, knowledge about how to appropriately design interdisciplinary education is still lacking. The current study presents the iterative development, pilot, and implementation of an interdisciplinary course in healthcare communication. Methods We used a design-based educational research approach in four phases to construct the program. In phase 1, we conducted a narrative review of the literature and distilled several prerequisites for interdisciplinary learning. In phase 2, we implemented two pilot courses with a focus on the content and the interdisciplinary context of the course. In research phase 3, we implemented the course during three consecutive years, with yearly evaluations of the course. In phase 4, we distilled design principles based on evaluation and reflection of the previous research phases. Results We elaborate on the various components of the design itself. Furthermore, using data from surveys, panel discussions and interviews, we reflect on the content and outcomes of the interdisciplinary course. We propose seven evidence-informed ‘crossing the line’ design principles for future interdisciplinary education. Conclusions The developed design principles pertain to interdisciplinary education in general and transcend subject-specific boundaries. The design principles are applicable in a wide range of higher education disciplines.

Do children match described probabilities? The sampling hypothesis applied to repeated risky choice

One way in which children can learn about probabilities of different outcomes before making a decision is from description, for instance, by observing graphical representations of frequency distributions. But how do repeated risky choices develop in early childhood when outcome probabilities are learned from description? Integrating previous findings from children’s sampling processes in causal learning and adults’ repeated choice behavior, we investigated repeated choices from 201 children aged 3 to 7 years and 100 adults in a child-friendly risky choice task. We expected young children to probability match and predicted that the perceived dependency between choices would shape the underlying choice process. However, the assumed cognitive processes derived from the causal learning and risky choice literature did not generalize to children’s or adults’ repeated risky choices when outcome probabilities were learned from graphical representations prior to making a decision. Moreover, choice behavior did not differ as a function of the perceived dependency between guesses. Instead, children broadly diversified choices, and switching between options dominated older children’s choice behavior. Our results contribute to increasing evidence of childhood as a phase for heightened exploration and highlight the importance of considering the learning format when studying repeated choice across development.

Comparison of asynchronous online versus in-person library instruction methods for teaching literature searching to graduate students

Introduction: The objectives of this comparative study were 1) to compare the effectiveness of in-person classroom instruction versus an online video for teaching literature searching skills to graduate students, and 2) to evaluate their perceptions of the instruction methods received and their instruction preferences for learning literature searching skills. Methods: Students enrolled in a translational medicine graduate course in Fall 2022 were invited to participate in the study. Participants were randomly assigned to a control group (receiving in-person instruction) or to an intervention group (receiving a link to a narrated PowerPoint video). Using Qualtrics online survey tool, participants completed one pre-test and two post-tests to assess learning and retention, and a survey to evaluate their perceptions and attitudes. Results: 12 out of 17 students participated. Both instruction methods were effective for delivering this information literacy content. The in-person group improved more than the online video group from pre- to post-test 1 and from pre- to post-test 2, but the difference was not statistically significant. However, the online video group rated the pace, perceived effectiveness, and clarity of library instruction, and their confidence to perform specific search tasks, more favourably than the in-person group, although the difference was not significant. Participants valued being able to access library training on their own schedule most of all. Discussion: The sample size for this study was small, making it difficult for differences to attain statistical significance. Creating an online video to deliver this content ended up being very time-intensive compared to providing synchronous instruction.

Autonomy-supportive instructional language does not enhance skill acquisition compared to controlling instructional language.

Instructional language is one of three techniques in OPTIMAL theory that can be manipulated to foster an autonomy-supportive practice environment to enhance motor performance and learning. While autonomy-supportive language has been shown to be beneficial in educational psychology, coaching, and health settings, the wording of task instructions has received minimal attention in the motor learning literature to date. We investigated the influence of two instructional language styles on skill acquisition in a preregistered experiment. Participants (N = 156) learned a speed cup stacking task and received instructions throughout practice that used either autonomy-supportive or controlling language. Although the autonomy-supportive instructions resulted in higher perceptions of autonomy, there were no group differences for motor performance in acquisition or retention. Perceptions of competence and intrinsic motivation did not differ between groups at any time point. These data are difficult to reconcile with key predictions in OPTIMAL theory regarding a direct and causal influence of motivational factors on performance and learning. However, our equivalence test suggests these effects on skill acquisition may be smaller than what we were powered to detect. These findings are consistent with a growing body of evidence highlighting the need for much larger N experiments in motor learning research.

Dynamical systems-inspired machine learning methods for drought prediction

Drought is a naturally occurring phenomenon that affects millions of people and results in billions of dollars in damages each year, with impacts expected to worsen due to climate change. At the same time, definitions of drought are nebulous, and extant quantitative drought indicators suffer from short prediction horizons. One such indicator is the Normalized Vegetation Difference Index (NDVI), which measures photosynthetic activity, making it a strong proxy for vegetation density. Recent studies have identified chaotic attractors in satellite-derived NDVI time-series, suggesting a dynamical systems framework may be helpful for time-series prediction of NDVI. In this study, we compare the performance of a mechanistic model and two physics-informed machine learning methods (Sparse Identification of Nonlinear Dynamics [SINDy] and reservoir computing) on the prediction of NDVI time-series data in drought-prone regions of Kenya. We find that SINDy, a sparse polynomial modelling architecture, narrowly outperforms the other two methods with the use of precipitation data, while also retaining some of the interpretability of the mechanistic model. We also find that none of the methods perform as well in the regions in which the chaotic NDVI attractors were originally identified. We conclude by proposing more sophisticated extensions to the methods presented here, both with and without the availability of precipitation data, that draw on the existing dynamical systems and machine learning literature to enable better quantitative predictions of key drought indicators.

Large Sample Behavior of the Least Trimmed Squares Estimator

The least trimmed squares (LTS) estimator is popular in location, regression, machine learning, and AI literature. Despite the empirical version of least trimmed squares (LTS) being repeatedly studied in the literature, the population version of the LTS has never been introduced and studied. The lack of the population version hinders the study of the large sample properties of the LTS utilizing the empirical process theory. Novel properties of the objective function in both empirical and population settings of the LTS and other properties, are established for the first time in this article. The primary properties of the objective function facilitate the establishment of other original results, including the influence function and Fisher consistency. The strong consistency is established with the help of a generalized Glivenko–Cantelli Theorem over a class of functions for the first time. Differentiability and stochastic equicontinuity promote the establishment of asymptotic normality with a concise and novel approach.

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration.

Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term "data hungriness" refers to the sample size needed for a modelling technique to generate a prediction model with minimal optimism. Our objective was to compare the relative data hungriness of different statistical and machine learning methods when assessed using calibration. We used Monte Carlo simulations to assess the effect of number of events per variable (EPV) on the optimism of six learning methods when assessing model calibration: unpenalized logistic regression, ridge regression, lasso regression, bagged classification trees, random forests, and stochastic gradient boosting machines using trees as the base learners. We performed simulations in two large cardiovascular datasets each of which comprised an independent derivation and validation sample: patients hospitalized with acute myocardial infarction and patients hospitalized with heart failure. We used six data-generating processes, each based on one of the six learning methods. We allowed the sample sizes to be such that the number of EPV ranged from 10 to 200 in increments of 10. We applied six prediction methods in each of the simulated derivation samples and evaluated calibration in the simulated validation samples using the integrated calibration index, the calibration intercept, and the calibration slope. We also examined Nagelkerke's R2, the scaled Brier score, and the c-statistic. Across all 12 scenarios (2 diseases × 6 data-generating processes), penalized logistic regression displayed very low optimism even when the number of EPV was very low. Random forests and bagged trees tended to be the most data hungry and displayed the greatest optimism. When assessed using calibration, penalized logistic regression was substantially less data hungry than methods from the machine learning literature.

Shifting Under Pressure: Timing Practices within Regulatory Information Disclosure Programs

Research examines the process through which regulatory agencies and other powerful third parties publicly disseminate information about organizations in order to reduce the information asymmetry between those organizations and their dispersed or less powerful stakeholders. These disclosure programs are presumed to work by mobilizing stakeholders to pressure organizations for change. Prior studies focus on organizations’ resistance to that external pressure, often drawing from theories of institutional forces and organizational legitimacy. Instead, the present study examines the public disclosure process itself, and adapts research on organizational learning and collective attention focus to develop new theory regarding how organizations may selectively time their practices to influence these disclosures in their favor. Results suggest that organizations may respond to disclosure programs and other governance structures when actively monitored, but may drift out of compliance during intervening periods. Findings also suggest that strong governance outside of the regulatory disclosure process can help to reduce these problems. The study contributes to corporate governance, organizational learning, and other literatures by developing new theory regarding the timing of organizational responses to regulatory interventions, and by providing evidence that overlapping forms of oversight may mitigate the limitations within any one particular governance mechanism.