Purpose -The purpose of this paper is to look at the problem caused by the operation of break clauses contained in commercial leases -a predominantly UK phenomenon, a consequence of longer lease terms. Design/methodology/approach -The pitfalls that can befall a corporate occupier are numerous and the authors of this paper share some of their recent experiences to highlight issues that can arise and how a Corporate Real Estate Manager or advisor can avoid or minimise those risks. Findings -For corporate occupiers, the operation of a break clause can be fraught with difficulty and its successful implementation requires a strategy to be put in place well in advance of the break date. Originality/value -The paper shows how turmoil in the wider financial market could make the flexibility that breaks offer very important to certain businesses.

In this paper, we analyse a large, opportunistic dataset of responses (N = 219,826) to online, diagnostic multiple-choice mathematics questions, provided by 6–16-year-old UK school mathematics students (N = 7302). For each response, students were invited to indicate on a 5-point Likert-type scale how confident they were that their response was correct. Using demographic data available from the online platform, we examine the relationships between confidence and facility (the proportion of questions correct), as well as gender, age and socioeconomic disadvantage. We found a positive correlation between student confidence and mean facility, higher confidence for boys than for girls and lower confidence for students classified as socioeconomically disadvantaged, even after accounting for facility. We found that confidence was lower for older students, and this was particularly marked across the primary to secondary school transition. An important feature of the online platform used is that, when students answer a question incorrectly, they are presented with an analogous question about 3 weeks later. We exploited this feature to obtain the first evidence in an authentic school mathematics context for the hypercorrection effect (Butterfield & Metcalfe J EXP PSYCHOL 27:1491–1494, 2001), which is the observation that errors made with higher confidence are more likely to be corrected. These findings have implications for classroom practices that have the potential to support more effective and efficient learning of mathematics.

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation.

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