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"teaching-learning case"
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Fostering Evidence-Based Education with Learning Analytics: Capturing Teaching-Learning Cases from Log Data
Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners' workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts-one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners.
Journal Article
A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy
In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.
Journal Article
How students come to be, know, and do : a case for a broad view of learning
\"Studies of learning are too frequently conceptualized only in terms of knowledge development. Yet it is vital to pay close attention to the social and emotional aspects of learning in order to understand why and how it occurs. How Students Come to Be, Know, and Do builds a theoretical argument for and a methodological approach to studying learning in a holistic way. The authors provide examples of urban fourth graders from diverse cultural and linguistic backgrounds studying science as a way to illustrate how this model contributes to a more complete and complex understanding of learning in school settings. What makes this book unique is its insistence that to fully understand human learning we have to consider the affective-volitional processes of learning along with the more familiar emphasis on knowledge and skills. Developing interest, persisting in the face of difficulty, actively listening to others' ideas, accepting and responding to feedback, and challenging ideas are crucial dimensions of students' experiences that are often ignored\"--Provided by publisher.
Book
Digital twin‐based production logistics resource optimisation configuration method in smart cloud manufacturing environment
To adapt to the dynamic, diverse, and personalised needs of customers, manufacturing enterprises face the challenge of continuously adjusting their resource structure. This has led manufacturers to shift towards a smart cloud manufacturing mode in order to build highly flexible production logistics (PL) systems. In these systems, the optimal configuring of PL resources is fundamental for daily logistics planning and vehicle scheduling control, providing necessary resources for the entire PL segment. However, traditional resource configuration methods face limitations, such as incomplete information acquisition, slow response in resource configuration, and suboptimal configuration results, leading to high subsequent operational costs and inefficient logistics transportation. These issues limit the performance of the PL system. To address these challenges, the authors propose a digital twin‐based optimisation model and method for smart cloud PL resources. The approach begins with constructing an optimisation model for the PL system considering the quality of service for a cloud resource is constructed, aiming to minimise the number of logistics vehicles and the total cost of the PL system. Additionally, a DT‐based decision framework for optimising smart cloud PL resources is proposed. Alongside a DT‐based dynamic configuration strategy for smart cloud PL resources is designed. By developing a multi‐teacher grouping teaching strategy and a cross‐learning strategy, the teaching and learning strategies of the standard teaching‐learning‐based optimisation algorithm are improved. Finally, numerical simulation experiments were conducted on the logistics transportation process of a cooperating enterprise, verifying the feasibility and effectiveness of the proposed algorithms and strategies. The findings of this study provide valuable references for the management of PL resources and algorithm design in advanced manufacturing modes. The authors introduce a digital twin‐based PL resource optimisation allocation model for improving smart cloud production logistics transportation, focusing on minimising vehicle numbers and total system costs. It presents a decision framework and dynamic allocation strategy for these resources, enhancing standard teaching‐learning‐based optimisation algorithms through multi‐teacher and cross‐learning strategies. The effectiveness of these methods is demonstrated through numerical simulations in a real‐world enterprise logistics scenario.
Journal Article
Optimizing smart manufacturing system: a digital twin approach utilizing teaching-learning-based optimization
This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching-learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation and analysis. By leveraging the TLBO algorithm, the system enhances the decision-making process for complex manufacturing tasks, facilitating continuous improvement and adaptation to dynamic production demands. The proposed framework aims to minimize production costs, reduce downtime and improve overall efficiency by optimizing key parameters such as resource allocation, production scheduling and machine performance. Experimental results demonstrate that the DT-TLBO approach can reduce production costs by up to 20%, decrease downtime by 30% and improve overall system efficiency by 25%. This innovative combination of advanced technologies offers a promising solution for modern manufacturing challenges, paving the way for smarter, more responsive production environments.
Journal Article
Eliciting Engagement in the High School Classroom: A Mixed-Methods Examination of Teaching Practices
This case study analyzes how and why student engagement differs across 581 classes in one diverse high school. Factor analyses of surveys with 1,132 students suggest three types of engaging teaching practices—connective instruction, academic rigor, and lively teaching. Multilevel regression analyses reveal that connective instruction predicts engagement more than seven times as strongly as academic rigor or lively teaching. Embedded case studies of five classes use interviews and observations to examine how various classes combine connective instruction, academic rigor, and lively teaching and how these practices individually and collectively engage students. Across these analyses, this study introduces a typology for thinking systematically about teaching for engagement.
Journal Article
A Validity Argument Approach to Evaluating Teacher Value-Added Scores
Value-added models have become popular in research and pay-for-performance plans. While scholars have focused attention on some aspects of their validity (e.g., scoring procedures), others have received less scrutiny. This article focuses on the extent to which value-added scores correspond to other indicators of teacher and teaching quality. The authors compared 24 middle school mathematics teachers' value-added scores, derived from a large (N = 222) district data set, to survey- and observation-based indicators of teacher quality, instruction, and student characteristics. This analysis found teachers' value-added scores correlated not only with their mathematical knowledge and quality of instruction but also with the population of students they teach. Case studies illustrate problems that might arise in using value-added scores in pay-for-performance plans.
Journal Article