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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

For over 30 years, the notion of authentic assessment in higher education has been adopted in academic practice, but it has managed to escape substantial critique. Although there have been multiple definitions and operationalisations of authentic assessment, current practice tends to foreground mimicking of work tasks. Authenticity cannot be completely unmoored from the reality of workplaces, the demands of the discipline, and the overall intended learning outcomes, however, a restricted view of how these aspects are represented in assessment can limit the sector's ability to prepare graduates who can engage with and shape the changing world. This paper elaborates the multiple challenges that some conceptualisations of authentic assessment contribute to assessment planning, recognising that assessment design always requires compromise. Three theoretical perspectives on authenticity are introduced to open new possibilities for authenticity in assessment. These are (1) psychological authenticity; (2) ontological fidelity; and (3) practice theory perspectives. The final section discusses how learning design might offer a means to operationalise theory. The paper concludes by suggesting that authenticity in assessment continues to hold value for the sector beyond its current uses through stronger theoretical conceptualisation and operationalisation of authenticity. [ABSTRACT FROM AUTHOR]

Digital games (DGs) have the potential to immerse learners in simulated real‐world environments that foster contextualised and active learning experiences. These also offer opportunities for performance assessments by providing an environment for students to carry out tasks requiring the application of knowledge and skills learned in the classroom. Although widely used for teaching and learning, there are challenges in using digital game applications for assessment. Designing assessments around tasks in complex learning environments like DGs is not always easy, particularly for educators new to using these technologies for teaching. This paper presents a game‐based assessment framework (GBAF) that is designed to be educator‐friendly and useful for the design of assessments for immersive learning environments. The application of the framework to the design of embedded and external (multiple‐choice questions) assessments is also presented. The GBAF offered an easy and structured basis for the design of two distinct assessments for a computer game. It provides educators with useful guidelines for designing assessments around pre‐existing games. Practitioner notesWhat is already known about this topicImmersive technologies are increasingly being used for teaching and learning due to their benefits to knowledge and skills acquisition. However, for educators interested in using these tools for formal classroom teaching, there is a lack of resources and guidance on assessment designs and implementations. The most commonly used assessment design framework, the evidence‐centred design (ECD), is complicated to use, requiring advanced machine learning skills to implement. This has so far limited its use to large‐scale computerised and game‐based testing.What this paper addsThis paper presents a framework developed to guide educators through the design and implementation of assessments when teaching with immersive learning applications. Grounded in the constructive alignment principles and the ECD framework, the game‐based assessment framework is designed to help educators align assessment tasks to game tasks and the intended learning outcomes. It also provides step‐by‐step guidelines for the identification of appropriate immersive applications. It shows the practical application of the framework to the design of embedded (in‐game) assessments and external (multiple‐choice tests) assessments.Implications for practice and/or policyThe framework presented in this paper provides a structured and pedagogically sound basis for the design of assessments when measuring performance, or skills and knowledge acquisition with immersive learning applications. [ABSTRACT FROM AUTHOR]

This paper presents a new integrated model for the multi-skilled project scheduling and multi-project scheduling problems, where the activities are technically linked through the generalized precedence relations (GPR). The proposed model is energy-efficient since the concerns regarding the energy consumptions of projects have been embraced as well. Reviewing the pertinent literature has revealed that the energy-efficient formulations for the multi-skill and multi-project scheduling problems with the GPR connections are very rare. For this formulation, two minimization objectives have been defined: (1) the overall duration to finish all projects and (2) the total energy consumption of all projects. This research offers a new version of the multi-objective vibration damping optimization (MOVDO) method to solve the proposed model. For the MOVDO, a new crossover-like operator has been designed that generates offspring solutions through the swapping procedure. Furthermore, a new population-updating strategy inspired by the Toom's rule cellular automaton has been devised for the MOVDO. In this strategy, each solution of the population can be replaced with its neighbor solutions based on the dominance criterion and the rules of the Toom's cellular automaton. Three other solution methodologies have been hired to solve the model in order to have a fair judgment on the efficacy of the MOVDO. The comparisons between the MOVDO and other optimizers have been conducted based on five performance assessment metrics. The results demonstrate the remarkable dominance of the MOVDO over other algorithms. [ABSTRACT FROM AUTHOR]

In early 2020, Initial Teacher Education providers were forced to reimagine many long-established practices due to pandemic restrictions. One post-primary concurrent Initial Teacher Education programme in the Republic of Ireland responded by conceptualising and developing an initiative to engage pre-service teachers in an authentic assessment task through the preparation of a classroom-based assessment from the perspective of the pupil. They then engaged in peer review and peer feedback on these classroom-based assessments and partook in a Subject Learning and Assessment Review meeting. This required the professional development of assessment literacies on the part of pre-service teachers, as well as disturbing their personal assessment beliefs at a cognitive and affective level. This paper finds that the collaborative assignment experience challenged pre-service teachers' previous conceptions about the purposes of assessment while providing them with insight and preparation for formative assessment processes in use throughout Irish post-primary schools. [ABSTRACT FROM AUTHOR]

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations. [ABSTRACT FROM AUTHOR]

Key digested message This article examines the expansive growth of ChatGPT and the implications for large-scale test design. The authors contend that the impressive test simulation results observed by Chat-GPT undergird ongoing construct validity concerns with student testing. In order to address these challenges, a set of strategies is proposed that emphasises authentic assessment, the importance of human elements in traditional paper-and-pencil questions, and the controversial issue of the stakes ascribed to test results. Collectively, these approaches are meant to help test developers more carefully consider existing limitations within traditional standardised and large-scale assessment programs. Ultimately, test design reforms that enhance validity are increasingly needed to address the challenges posed by AI applications. [ABSTRACT FROM AUTHOR]

The importance of creativity for survival in modern society is well recognised. However, the development of creative thinking skills through formal education still needs more attention, and the assessment of creative thinking skills using valid models in higher education is under-researched. Our paper presents the deficits and improvements in first-year business and economics undergraduates' creative thinking skills using an authentic assessment task (product). We assessed creativity under three broad themes: creative expression, knowledge creation and creative problem solving, as presented in the OECD Programme for International Student Assessment (PISA). These themes were chosen to reflect students' domain-specific expertise, motivations and creative thinking skills. Our analysis finds areas where students have deficits in creative thinking skills and how they have improved those skills through the assessment task. These findings imply the importance of providing students with opportunities to enhance creative thinking, mainly through the application of their learning. Our study provides insights into pedagogical strategies and assessment practices to enhance the creativity required from graduates in any field. [ABSTRACT FROM AUTHOR]