Learning Curve Theory Research Articles

Knowledge Graph of Low-Carbon Technologies in the Energy Sector and Cost Evolution Based on LDA2Vec: A Case Study in China

Climate change has attracted global attention, highlighting the critical role of low-carbon technologies in addressing environmental challenges. Due to the multidisciplinary nature, complexity, and diversity of research content on low-carbon technologies, a comprehensive overview is still limited. This paper uses bibliometrics analysis to discuss the research status and hotspots of low-carbon technology from a macro-perspective. The LDA2Vec topic recognition model is adopted to identify key technical terms, and CiteSpace software 6.3.1 Advanced Edition is used to conduct in-depth analysis of the development trajectory of low-carbon technology. After checking the frequency of the relevant keywords, four key techniques were identified. In order to further analyze the research results, the learning curve theory is used to predict the cost development trend of key low-carbon technologies. The results show that: (i) low-carbon technologies play a key role in the energy sector and have a potential impact on policy making, and the cost of related technologies will be significantly reduced in the next few years. (ii) Global low-carbon technologies have entered an important period of development, but remaining challenges need to be addressed by optimizing technological performance. (iii) It is very important to strengthen the research on hydrogen production technology and photovoltaic power generation technology; the cost reduction in hydrogen production technology is still significant and there is room for further optimization. (iv) To effectively address the high costs and technical barriers associated with emerging low-carbon technologies, increased funding for research and development is critical.

Water availability may not restrict the implementation of carbon capture utilization and storage technology: Evidence from China's electric power sector

Carbon capture, utilization and storage (CCUS), a water resource-intensive technology, has been projected to be restricted by water resources in the future. However, the water-saving effects resulting from technology progress and CO2-Enhanced water recovery (CO2-EWR) process are not been fully considered. Based on the learning curve theory, this study proposes a new water accounting system for CCUS retrofitting of coal-fired power plants (CFPPs) from a dynamic perspective considering power generation, carbon capture, and CO2-EWR technology. The results show that the total water withdrawal per unit of CCUS retrofitting with the first-generation capture technology will decrease by 66.89 %, and the total water consumption per unit of that will decrease by 74.70 % from 2021 to 2050. By contrast, the water withdrawal and consumption per unit with the second-generation capture technology will decrease by 74.12 % and 82.51 %, with the overall unit water consumption below 0.5 m3/MWh. Moreover, the water usage caused by CCUS retrofitting will be greatly relieved through the progress of geological sequestration technology (especially the CO2-EWR). In addition, the water usage during the capture process for circulation cooling and air-cooling would present an “inverted U” shaped trend affected by the capture scale and technology progress. Remarkably, the water withdrawal and consumption intensity of CFPPs with air-cooling technology is significantly lower than those of the circulation cooling technology for power generation, while the opposite happens if the capture technology is applied. Even so, CCUS retrofitting for the CFPPs with air-cooling technology can save 60 × 104 m3 of water resource after 2040, which is positive for the CCUS deployment in the arid areas of China.

Learning Curve as Effective Approach to Study Efficiency and Productivity in Business Process

Learning is a process/technique increasing knowledge or skill through study or experience. Learning helps to become aware of situation/state through observing or hearing about whether it is process, functioning, operational procedure etc.. The learning curve refer as the experience curve, the cost curve, the efficiency curve, or the productivity curve. It is in various form increasing, diminishing learning curve how a process(Business/Operation) is improved during time due to learning and increased proficiency/efficiency. The learning curve theory explains, it is a process which helps to study the task performed is taking less time and resources used and productivity depends upon performance due to competency/efficiency as process will be learned. A learning curve is explained the rate of improvement in techniques and gives best result in production process. In business, the slope of the learning curve represents the rate at which learning new skills/knowledge add cost-savings technique in business. In this article, the focus is on to study types and application of learning curve in business as efficiency and experience effect cost and productivity respectively.

Earnings management: a three-decade analysis and future prospects

PurposeThis paper undertakes an extensive and systematic review of the literature on earnings management (EM) over the past three decades (1992–2022). Furthermore, the study identifies emerging research themes and proposes future avenues for further investigation in the realm of EM.Design/methodology/approachFor this study, a comprehensive collection of 2,775 articles on EM published between 1992 and 2022 was extracted from the Scopus database. The author employed various tools, including Microsoft Excel, R studio, Gephi and visualization of similarities viewer, to conduct bibliometric, content, thematic and cluster analyses. Additionally, the study examined the literature across three distinct periods: prior to the enactment of the Sarbanes-Oxley Act (1992–2001), subsequent to the implementation of the Sarbanes-Oxley Act (2002–2012), and after the adoption of International Financial Reporting Standards (2013–2022) to draw more inferences and insights on EM research.FindingsThe study identifies three major themes, namely the operationalization of EM constructs, the trade-off between EM tools (accrual EM, real EM and classification shifting) and the role of corporate governance in mitigating EM in emerging markets. Existing literature in these areas presents mixed and inconclusive findings, suggesting the need for further theoretical development. Further, the study findings observe a shift in research focus over time: initially, understanding manipulation techniques, then evaluating regulatory measures, and more recently, investigating the impact of global accounting standards. Several emerging research themes (technology advancements, cross-cultural and cross-national studies, sustainability, behavioral aspects and non-financial indicators of EM) have been identified. This study subsequent analysis reveals an evolving EM landscape, with researchers from disciplines like data science, computer science and engineering applying their analytical expertise to detect EM anomalies. Furthermore, this study offers significant insights into sophisticated EM techniques such as neural networks, machine learning techniques and hidden Markov models, among others, as well as relevant theories including dynamic capabilities theory, learning curve theory, psychological contract theory and normative institutional theory. These techniques and theories demonstrate the need for further advancement in the field of EM. Lastly, the findings shed light on prominent EM journals, authors and countries.Originality/valueThis study conducts quantitative bibliometric and thematic analyses of the existing literature on EM while identifying areas that require further development to advance EM research.

THE READABILITY OF KEY AUDIT MATTERS PARAGRAPH IN AUDITOR'S REPORT: A STUDY FROM ASEAN COUNTRIES

The objective of this research is to explore the readability level of the Key Audit Matters (KAM) paragraph as well as its progress since the first time implementation of KAM for financial statements issued by auditors for listed companies in ASEAN countries. KAM paragraph is extracted from an audit opinion issued by a public accounting firm for companies listed in the capital market around ASEAN countries. Following prior research, by using learning curve theory as a basis, this research applies a quantitative approach to evaluate the readability of the KAM and its progress. Readability is measured by using several commonly used linguistic techniques, which are Flesch Reading Ease (FLESCH), Flesch–Kincaid (KINCAID), and Gunning Fog (FOG) measures). In addition to its role in KAM topic-related research, by bringing the perspective to focus more on readability, this research is able to provide new perspectives on the capability of KAM to affect financial statements users' decisions, as well as provide additional insight for the regulator or professional body for public accountant profession on designing the practical support and assistance to enhance the current practice of KAM writing done by the public accountant, as well as insight for the public accounting firm in preparing clearer and readable KAM paragraphs. This research used ASEAN market data as an emerging market and covered up to the recent year auditor's report to study the readability of the KAM paragraph. Despite some inconsistencies in the observation result, we can make an inference that the readability level of KAM produced by auditors for listed entities in ASEAN countries tend to be more difficult to read as number of experiences in issuing it increasing. This pattern is consistently shown when the sample is divided by type of Auditor and country of exchange. However, when the sample is further detailed down by industry sector, inferences is difficult to be made.

Researching models of innovation and adoption in health professions education.

Despite the constant presence of change and innovation in health professions education (HPE), there has been relatively little theoretical modelling of such change, the experiences of change, the ideology associated with change or the unexpected consequences of change. In this paper, the authors explore theoretical approaches to the adoption of innovations in HPE as a way of mapping a broader theoretical landscape of change. The authors, HPE researchers with an interest in technology adoption and systemic change, present a narrative review of the literature based on a series of thought experiments regarding how communities and individuals respond to the introduction of new ideas or methods. This research investigates the stages of innovation adoption, from the emergence and hype around new ideas to the concrete experiences of early adopters. When an innovation first emerges, there is often little concrete information available to inform potential adopters, leaving it susceptible to hype, both positive and negative. This can be described using the Gartner Hype Cycle model, albeit with important caveats. Once the adoption of an innovation gets underway, early adopter user experiences can inform those that follow. This can be described using Rogers' diffusion of innovation model, again with caveats. Notably, neither model goes beyond the point of single point-in-time, yes/no, individual adoption. Other approaches, such as learning curve theory, are needed to track uptake and maintenance by individuals over time. This expanded theoretical base, while still somewhat instrumentalist, combined with complementary theoretical perspectives can afford opportunities to better explore reasons for variance, volunteerism and resistance to change. In summary, change is complicated and nuanced, and better models and theories are needed to understand and work meaningfully with change in HPE. To that end, the authors seek to encourage richer and more thoughtful research and scholarly thinking about change and a more nuanced approach to the pursuit of change in HPE as a whole.

Identification of Learning Effects in Modular Construction Manufacturing

The effects of learning in real-life modular construction (MC) factories and their application for productivity improvement are not well understood, despite the shift from on-site construction to factory-based MC facilitated by automation and robotics. Informed by learning curve theory, this paper describes the learning effects of modular construction manufacturing (MCM) and their implications for MCM management. The Stanford-B model was identified as the best fit when comparing four learning curves in relation to a Hong Kong case study, revealing that the learning rate in modular construction manufacturing (MCM) fluctuates rather than following a linear pattern. These learning effects in MCM bridge the gap between the disorder of on-site construction and the structured production of manufactured goods. This research unveils the previously unexplored “black box” of learning effects in MCM, unlocking the potential offered by MC.

IFRS diffusion and earnings quality: Moderating role of firm size and IFRS specialists

AbstractThe study investigates the learning curve impact of International Financial Reporting Standards (IFRS) adoption on earnings quality. In other words, the study investigates how IFRS affects the quality of earnings over time. The study also examines the moderating role of firm size and IFRS specialists/experts on the issue. We use firm‐level data of Bombay Stock Exchange listed firms and analyze it through the “Difference in Difference” technique. We find that the association between IFRS and earnings quality changes favorably as the introduction grows longer. In particular, we find that earnings quality is lower during the initial years of IFRS adoption, however, it improves in the later years of adoption, consistent with the notion of diffusion and learning curve theory. Our subsequent tests suggest that small firms are taking more time to percolate through the IFRS system, however, it does not hold when small firms hire IFRS specialists/experts to migrate their older accounts to IFRS, consistent with the notion of resource dependency theory of innovation. Overall, IFRS is found to be a continuous improvement process in emerging nations whose benefit can be derived in the initial years by providing sufficient infrastructural and institutional support, particularly to small firms. Our findings are robust to the problem of endogeneity and offer real‐world ramifications for regulatory authorities, accounting‐standard setters, and investors.

Deep knowledge tracing with learning curves.

Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model and a Capsule-Enhanced CAKT (CECAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept, and CECAKT improves CAKT by replacing the global average pooling layer with capsule networks to prevent information loss. Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT and CECAKT both achieve better performance compared to existing deep KT models.

A novel life cycle assessment and life cycle costing framework for carbon fibre-reinforced composite materials in the aviation industry

PurposeCarbon fibre-reinforced composite materials offer superior mechanical properties and lower weight than conventional metal products. However, relatively, little is known about the environmental impacts and economic costs associated with composite products displacing conventional metal products. The purpose of this study is to develop an integrated life cycle assessment and life cycle costing framework for composite materials in the aviation industry.MethodsAn integrated life cycle assessment (LCA) and life cycle costing (LCC) framework has been developed. The displacement of a conventional aluminium door for an aircraft by a composite door is presented as an example of the use of this framework. A graphical visualisation tool is proposed to model the integrated environmental and economic performances of this displacement. LCA and LCC models for composite applications are developed accordingly. The environmental hotspots are identified, and the sensitivity of the environmental impact results to the different composite waste treatment routes is performed. Subsequently, the research suggests a learning curve to analyse the unit price for competitive mass production. Sensitivity analysis and Monte Carlo simulation have been applied to demonstrate the cost result changes caused by data uncertainty.ResultsEnergy consumption was the hotspot, and the choice of composite waste treatment routes had a negligible effect on the LCA outcomes. Concerning the costs, the most significant cost contribution for the unit door production was labour. The future door production cost was decreased by about 29% based on the learning curve theory. The uncertainties associated with the variables could lead to variations in the production cost of up to about 16%. The comparison between the two doors shows that the composite door had higher potential environmental impacts and cost compared to the conventional aluminium door during the production stage. However, the composite door would have better environmental and financial performance if a weight reduction of 47% was achieved in future designs.ConclusionsThe proposed framework and relevant analysis models were applied through a case study in the aerospace industry, creating a site-specific database for the community to support material selection and product development. The graphical tool was proved to be useful in representing a graphical visualisation comparison based on the integration of the LCA and LCC results of potential modifications to the composite door against the reference door, providing understandable information to the decision-makers.

Modeling how fast teacher trainees master statistical concepts in verification of the learning curve theories

This study was undertaken to model and evaluate how fast Ghanaian teacher trainees master statistical concepts, in verification of the learning curve theory. The target population was all the 2020/2021 academic year level 300 teacher trainees in Ghana. However, St. Joseph’s College of Education (JOSCO), Bechem, was selected out of proximity from the 46 colleges of education in Ghana. At JOSCO, the Science/Mathematics, Agriculture/ICT and the Social Studies classes were randomly selected from the five level 300 classes and all the students in the three classes served as the experimental units. In effect, a ten-week period experimental tuition and evaluation on Educational Statistics was conducted and the data on the percentage of statistical concepts mastered by the teacher trainees were collected for the study. Tabular and graphical analyses on the data were carried out for the verification of the learning curve theory. Ordinary differential equations were used to derive the model for the prediction of the percentage of statistical concepts mastered by the teacher trainees. Graphical and numerical residual analyses were also conducted for the model adequacy checking. In all, the learning curve was successfully verified and the derived model was also found to be adequate for the prediction of the percentage of statistical concepts mastered by the teacher trainees at any point in time. This happens to be the first time research has been successfully conducted to verify the learning curve theory in statistical concept build-up in the colleges of education in Ghana.

Technical and economic analysis of different colours of producing hydrogen in China

This paper explores the different types of hydrogen production by exploring them and giving each a different colour. From dark to money in descending order, they represent a gradual reduction in pollution. The basic principles, advantages and disadvantages of the respective production methods are explored at a technical level. The learning curve theory calculates the individual learning rates for different hydrogen production methods to increase the installed capacity while reducing the capital investment and cost of hydrogen production. The study concludes that coal-based hydrogen production is currently the primary method of hydrogen production in China in the short term due to its high resource endowment of coal. As CCUS technology matures, converting brown hydrogen to blue hydrogen and grey hydrogen to turquoise hydrogen are the two more economically viable options. For the cleanest form of green hydrogen, the fundamental problems are the impediments of electrolyser technology (PEM, SOEC cannot be commercialised) and the high wind and photovoltaic power cost. The solution is to use wind and light abandonment to increase hydrogen production in the short term and replace fossil energy with renewable energy generation in the long term to achieve clean hydrogen production. For hydrogen energy, the vigorous development of hydrogen fuel cell vehicles and the massive reduction of greenhouse gas emissions in transport is essential to achieving carbon neutrality while ensuring the supply of hydrogens such as blue hydrogen and green hydrogen.

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.

Economic and environmental impacts of public investment in clean energy RD&D

Learning curve theory has been adopted for investigating the relationship between technological learning and technology cost developments. The aim of this paper is to explore the impacts of public investment in clean energy research, development, and demonstration (RD&D) on future technology cost developments by using a two-factor learning curve approach. Learning-by-deploying and learning-by-researching were chosen as the main sources of learning. The focus is on onshore wind and solar photovoltaics in the United States of America. By using publicly available data, we estimated learning-by-deploying rates of 31.4% and 27.6%; and learning-by-researching rates of 2.3% and 4.7% for onshore wind and solar PV, respectively. By adopting a logistic curve approach, an additional $1322 and $819 mil. were forecast to be spent by 2050 in RD&D for onshore wind and solar PV, respectively. We explored the plausible long-term effects of diverse RD&D investment scenarios on electricity generation and greenhouse gas (GHG) emissions using a system dynamics model. The findings reveal that public investment in RD&D for clean energy technologies may play a key role in the pace of capital cost reductions and technology diffusion. However, relatively little long-term effects of RD&D efforts alone were found on market dynamics and GHG emissions.

Effects of learning curve models on onshore wind and solar PV cost developments in the USA

Technological innovation planning for developing and deploying clean energy technologies plays a key role in reducing greenhouse gas emissions and transition to a low-carbon future. Learning curve theory has been adopted as a common framework for exploring the relationship between endogenous technological learning and technology cost developments. The aim of this article is to analyse the effects of selecting different learning curve approaches (i.e. model formulations) to describe energy technology cost changes over time. Experience and knowledge stock are chosen as the sources of learning to be considered. A new definition of experience was developed to account for the interaction between global and local experience. The new definition of experience also accounts for learning sub-processes (i.e. learning-by-doing, learning-by-using, and experience spillovers) to estimate total experience gained through technology deployment. An integrative model is developed for estimating the effects of learning-by-deploying and learning-by-researching on cost developments for onshore wind and solar PV in the USA. Publicly available data from government departments and organisations were utilised. It was found that technology cost developments are better explained when: (1) experience is defined as a function of global and local experience; (2) knowledge stock is also considered in the model formulation; and (3) technological processes affect only a fraction of the total capital cost. The findings suggested that the application of learning rates for model-based energy planning is context-dependent and how technological factors are explicitly defined may have significantly different policy implications (i.e. different technology costs predictions based on alternative model formulations).

Validity Evidence for Assessing Entrustable Professional Activities During Undergraduate Medical Education.

To explore validity evidence for the use of entrustable professional activities (EPAs) as an assessment framework in medical education. Formative assessments on the 13 Core EPAs for entering residency were collected for 4 cohorts of students over a 9- to 12-month longitudinal integrated clerkship as part of the Education in Pediatrics Across the Continuum pilot at the University of Minnesota Medical School. The students requested assessments from clinical supervisors based on direct observation while engaging in patient care together. Based on each observation, the faculty member rated the student on a 9-point scale corresponding to levels of supervision required. Six EPAs were included in the present analyses. Student ratings were depicted as curves describing their performance over time; regression models were employed to fit the curves. The unit of analyses for the learning curves was observations rather than individual students. (1) Frequent assessments on EPAs provided a developmental picture of competence consistent with the negative exponential learning curve theory; (2) This finding was true across a variety of EPAs and across students; and (3) The time to attain the threshold level of performance on the EPA for entrustment varied by student and EPA. The results provide validity evidence for an EPA-based program of assessment. Students assessed using multiple observations performing the Core EPAs for entering residency demonstrate classic developmental progression toward the desired level of competence resulting in entrustment decisions. Future work with larger data samples will allow further psychometric analyses of assessment of EPAs.

Learn Curve for Precast Component Productivity in Construction

The study objective is to establish the learning curve model for precast component productivity in construction, verified using cross-validation empirical data for over 90% of these facilities’ precast component production activities over the past 5 years, with a total of 373,077 datasets across 14 production activities, sorted among a total of 4352 workers. By applying the learning curve theory to the analysis, the results show that relative to the straight-line model, the learning curve was established using exponential models. The exponential model can effectively mitigate the unreasonable fluctuations present in the cubic model’s representations of learning curves during initial training periods. This study therefore suggests the adoption of the Exponential model to model the learning curves for production workers learning to make precast components. The model has a satisfactory degree of fit (R2 > 0.88), and the post-cross-validation results also show that the model has a highly accurate prediction capability (MAPE value < 10%). The finding can serve as an important reference for the creation of production personnel allocation plans, personnel reserve plans, and training plans at precast factories in the construction industry.

Determining Worker Training Time for Precast Component Production in Construction: Empirical Study in Taiwan

AbstractThis study is aimed at determining the worker training time and proficiency threshold for each activity in precast component production based on the learning curve theory. Training data for...

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates.

Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing

Knowledge tracing (KT) is essential for adaptive learning to obtain learners’ current states of knowledge for the purpose of providing adaptive service. Generally, the knowledge construction procedure is constantly evolving because students dynamically learn and forget over time. Unfortunately, to the best of our knowledge most existing approaches consider only a fragment of the information that relates to learning or forgetting, and the problem of making use of rich information during learners’ learning interactions to achieve more precise prediction of learner performance in KT remains under-explored. Moreover, existing work either neglects the problem difficulty or assumes that it is constant, and this is unrealistic in the actual learning process as problem difficulty affects performance undoubtedly and also varies overtime in terms of the cognitive challenge it presents to individual learners. To this end, we herein propose a novel model, KTM-DLF (Knowledge Tracing Machine by modeling cognitive item Difficulty and Learning and Forgetting), to trace the evolution of each learner’s knowledge acquisition during exercise activities by modeling his or her dynamic knowledge construction procedure and cognitive item difficulty. Specifically, we first specify the concept of cognitive item difficulty and propose a method to model the cognitive item difficulty adaptively based on learners’ learning histories. Then, based on two classical theories (the learning curve theory and the Ebbinghaus forgetting curve theory), we propose a method for modeling learners’ learning and forgetting over time. Finally, the KTM-DLF model is proposed to incorporate learners’ abilities, the cognitive item difficulty, and the two dynamic procedures (learning and forgetting) together. We then use the factorization machine framework to embed features in high dimensions and model pairwise interactions to increase the model’s accuracy. Extensive experiments have been conducted on three public real-world datasets, and the results confirm that our proposed model outperforms the other state-of-the-art educational data mining models.