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Learning at risk? Keys to improving educational results in Catalonia
No full textThe results of the latest PISA test shed concern on the learning levels of Catalan students. Catalonia has worsened its average scores in mathematics, reading and science and, above all, the inequalities between the results obtained by high SES students and low SES students have increased.
These poor results of Catalonia in the PISA tests are in line with those obtained by students in the 6th grade of primary school and 4th grade of ESO (lower secondary education) in the national basic competence assessments, where we observe:
Learning difficulties in mathematics increase throughout ESO. On the one hand, nine out of ten students who had a low performance in mathematics in the 6th grade tests continue to have it in the 4th grade ESO tests. On the other hand, more than 60% of the students with average and high performance the 6th grade tests, drop their level in the 4th grade tests.
Learning levels in Catalan language recover in the transition from primary to secondary, although it remains at a level of sufficiency that has not improved in the last ten years.
The educational inequalities between socioeconomic disadvantaged and non-disadvantaged schools remain chronic or even increase, becoming a burden for the improvement of results in the system as a whole.
A learning recovery plan is needed in Catalonia that focuses attention on students and the most vulnerable schools and prioritizes those actions that the evidence indicates are most effective for improving their skills. From an international comparative perspective, this contribution offers a diagnosis of the state of learning in Catalonia, of the main factors that hinder its improvement, and finally reviews the actions the educational research shows that can contribute the most to neutralizing them (support programs, funding formula for school resource allocations, or study financial aim schemes)
ELS ECONOMISTES DAVANT ELS REPTES QUE PLANTEJEN ELS CANVIS NORMATIUS I TECNOLÒGICS.
Get PDFUna reflexió sobre la irrupció de les noves tecnologies i la seva aplicació al desenvolupament de la professió d’economista. Reptes i oportunitats que suposa aquests canvis. Evolució normativa de determinades actuacions professionals i constatació d’alguns perills que pot suposar l’incorrecte incorporació d’aquests canvis
Catalonia’s Trade Strategy: Assessing the Impact of Trade Delegations and Other Agreements
Get PDFThis study investigates the determinants of Catalan trade flows by ex- amining the impacts of Preferential Trade Agreements, Bilateral Investment Treaties , and the activities of the Catalan governmental agency, ''Accio'', using a comprehensive dataset spanning from 1995 to 2020. The findings reveal that both PTAs and BITs generally foster trade. Furthermore the role of ''Accio ''as a facilitator of internationalization is also particularly significant, especially for differentiated goods
Top Reasons Why Nursing Students in Australia Choose Professional Assignment Help
Get PDFNursing students in Australia face a demanding and rigorous academic schedule that
often includes a mix of theoretical learning, clinical practice, and extensive
assignments. With the pressure to excel in all aspects of their studies, many nursing
students seek professional assistance to ensure they maintain high academic standards.
In this blog, we'll explore the top reasons why nursing students in Australia turn to
professional assignment help and how it can make a significant difference in their
educational journey.
1. Time Management and Workload
One of the primary reasons nursing students opt for professional nursing assignment
help Australia is the overwhelming workload. Nursing courses typically require
students to juggle multiple tasks at once, including lectures, clinical rotations, and
assignments. Given that assignments often have strict deadlines, managing them
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them to focus on other critical areas such as practical training or exam preparation.
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Nursing assignments require a high level of expertise, as they often involve complex
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Nursing is a broad field, covering various specializations such as pediatrics, geriatric
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The ultimate goal for most nursing students is to succeed academically and graduate
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Q-Learning-Based Lightweight Task Orchestrator: A Lightweight Q-Learning-Based Scheduler for Service Level Agreement-Aware Container Placement in Heterogeneous Clusters
No full textEfficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of Service (QoS), energy efficiency, and security, despite differences in resources and architectures. This research introduces Federated Q-Learning–based Lightweight Task Orchestrator (F-Q-LiTO), a compact and intelligent orchestration framework that combines predictive modeling, approximate data structures, and security filtering to enable adaptive task placement across distributed environments. Unlike complex deep reinforcement learning models such as Deep Reinforcement Model (DeepRM) or traditional heuristic schedulers like Kubernetes BinPacking, F-Q-LiTO uses tabular Q-learning enhanced with federated aggregation, which significantly reduces computational and communication overhead, making it ideal for edge computing environments with limited resources. The framework incorporates a Long Short-Term Memory (LSTM)–based predictor for proactive resource forecasting, a Count-Min Sketch for scalable resource utilization estimation, and an XOR filter for efficient and lightweight security enforcement. Experimental results demonstrate that F-Q-LiTO achieves 98.6% task completion, 96.8% SLA satisfaction, and reduces energy consumption to 180.5 kilowatt-hours (kWh). It outperforms DeepRM and Kubernetes by achieving 34% fewer missed deadlines and up to 30% lower energy imbalance. The system converges quickly—by Episode 6—and maintains cluster fairness (Jain’s Fairness Index= 0.98) along with priority-aware placement accuracy of 93.2%. Security analysis shows that F-Q-LiTO successfully blocks 98.5% of unauthorized task placements while using only 0.3 megabytes (MB) of memory. Overall, F-Q-LiTO demonstrates that a federated and lightweight reinforcement learning approach can deliver scalable, secure, and QoS-aware orchestration for modern edge and multi-cloud computing environments without compromising performance or efficiency.OPEN ACCESS Received: 24/07/2025 Accepted: 14/10/2025 Published: 15/12/202