'My rent was higher than my student loan' - the booming cost of private halls You don't have to walk far in any university city before spotting some form of private Purpose-Built Student Accommodation (PBSA). These large blocks of student housing are springing up in cities across the UK, yet rents continue to soar and young people studying at university say they're still finding it difficult to secure a place to live. The increase in PBSA has predominantly been driven by private companies, with many developments targeting more affluent international and postgraduate students, who industry experts say are becoming increasingly important to the sector. Developers say they are meeting a market demand but campaigners argue that they are too expensive for most Scottish students and more affordable housing should be being built instead. Twenty-year-old Lara Reader told the BBC that when she applied for student accommodation at Edinburgh University she did not expect to be allocated the most expensive option on her list.

Schools in England get extra £500m in bid to avoid teachers' pay strike Image caption, The largest teaching union had objected to the prospect of partly funding a 3.5% pay increase this year out of existing school budgets Schools in England are to get around £500 million in extra funding towards the teachers' pay award this school year, reducing the risk of a strike ballot by the largest education union. Schools had been expected to fund some of the 3.5% teachers pay increase for the current school year out of their existing budgets, which National Education Union (NEU) leaders had strongly objected to. Daniel Kebede, the General Secretary of the NEU, said the offer of more funding was a significant step in negotiations. In a letter to the union on Wednesday, which has been seen by the BBC, the government said the increase would be funded by savings in employers' contributions to the local government pension scheme. The letter said the 4.9 percentage point drop in what employers are expected to contribute will deliver significant savings on the pay bill for support staff, which will not be clawed back by central government.

University applicants in England will be given clearer information about student loans before they take them out, the government has said. Ministers said they would spell out that governments can change repayment rules and that different career choices can affect how much borrowers repay. They agreed to some but not all of the recommendations made by MPs after an inquiry into student loans, and did not say whether they would reverse a freeze to the repayment threshold for some graduates. The inquiry found the way loans had been presented to teenagers amounted to mis-selling, after the BBC revealed the Department for Education (DfE) had compared repayments to £30-a-month phone contracts. The recent debate has centred around Plan 2 loans, which were issued in England between September 2012 and July 2023, and are still issued in Wales.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Teachers, union members, attend a rally at Molina Grand Park in Los Angeles on Wednesday. United Teachers Los Angeles and Local 99 service workers announced members would strike on April 14, if no deal is reached before then. This is read by an automated voice. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. teachers union widely expected to announce strike date at massive Wednesday rally Members of the largest unions representing teachers and nonteachers participate in joint rally at Grand Park in March 2023. The scene will be repeated on Wednesday, with union members once again on the verge of a strike. This is read by an automated voice. Please report any issues or inconsistencies here .

AI tools have become deeply embedded in how many students learn and complete schoolwork--and that usage is only poised to increase. On Tuesday, the American Federation of Teachers announced an AI training hub for educators, backed by 23 million from Microsoft, OpenAI, and Anthropic. The AFT is the second-largest teachers' union, representing 1.8 million teachers and educational staffers across the country. Their training hub will open in New York City this fall, featuring workshops that will educate teachers on how to use AI tools for tasks like generating lesson plans and quizzes, or writing emails to parents. Microsoft is providing 12.5 million for AI teacher training over the next five years.

Microsoft and OpenAI are planning to announce Tuesday that they are helping to launch an AI training center for members of the second-largest teachers' union in the US, according to details about the initiative that appear to have been inadvertently published early on YouTube. The National Academy for AI Instruction will be based in New York City and aims to equip kindergarten up to 12th grade instructors in the American Federation of Teachers with "the tools and confidence to bring AI into the classroom in a way that supports learning and opportunity for all students," according to the description of a publicly accessible YouTube livestream scheduled for Tuesday morning. The YouTube page also lists Anthropic, which develops the Claude chatbot, as a collaborator on what's described as a 22.5 million initiative to bring free "AI training and curriculum" to teachers. The three AI companies and the union did not immediately respond to requests for comment about the information released on YouTube. On Monday, Microsoft and the union declined to share details ahead of an announcement planned for Tuesday morning in New York.

Knowledge Distillation (KD) has emerged as a pivotal technique for neural network compression and performance enhancement. Most KD methods aim to transfer dark knowledge from a cumbersome teacher model to a lightweight student model based on Kullback-Leibler (KL) divergence loss. However, the student performance improvements achieved through KD exhibit diminishing marginal returns, where a stronger teacher model does not necessarily lead to a proportionally stronger student model. To address this issue, we empirically find that the KL-based KD method may implicitly change the inter-class relationships learned by the student model, resulting in a more complex and ambiguous decision boundary, which in turn reduces the model's accuracy and generalization ability. Therefore, this study argues that the student model should learn not only the probability values from the teacher's output but also the relative ranking of classes, and proposes a novel Correlation Matching Knowledge Distillation (CMKD) method that combines the Pearson and Spearman correlation coefficients-based KD loss to achieve more efficient and robust distillation from a stronger teacher model. Moreover, considering that samples vary in difficulty, CMKD dynamically adjusts the weights of the Pearson-based loss and Spearman-based loss. CMKD is simple yet practical, and extensive experiments demonstrate that it can consistently achieve state-of-the-art performance on CIRAR-100 and ImageNet, and adapts well to various teacher architectures, sizes, and other KD methods.

Wi-Fi fingerprinting is widely applied for indoor localization due to the widespread availability of Wi-Fi devices. However, traditional methods are not ideal for multi-building and multi-floor environments due to the scalability issues. Therefore, more and more researchers have employed deep learning techniques to enable scalable indoor localization. This paper introduces a novel semi-supervised learning framework for neural networks based on wireless access point selection, noise injection, and Mean Teacher model, which leverages unlabeled fingerprints to enhance localization performance. The proposed framework can manage hybrid in/outsourcing and voluntarily contributed databases and continually expand the fingerprint database with newly submitted unlabeled fingerprints during service. The viability of the proposed framework was examined using two established deep-learning models with the UJIIndoorLoc database. The experimental results suggest that the proposed framework significantly improves localization performance compared to the supervised learning-based approach in terms of floor-level coordinate estimation using EvAAL metric. It shows enhancements up to 10.99% and 8.98% in the former scenario and 4.25% and 9.35% in the latter, respectively with additional studies highlight the importance of the essential components of the proposed framework.

In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.