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Multi-temperature X-ray diffraction studies show that twisting, rotation, and libration cause negative thermal expansion (NTE) of the nanoporous metal−organic framework MOF-5, Zn4O(1,4-benzenedicarboxylate)3. The near-linear lattice contraction is quantified in the temperature range 80−500 K using synchrotron powder X-ray diffraction. Vibrational motions causing the abnormal expansion behavior are evidenced by shortening of certain interatomic distances with increasing temperature according to single-crystal X-ray diffraction on a guest-free crystal over a broad temperature range. Detailed analysis of the atomic positional and displacement parameters suggests two contributions to cause the effect: (1) local twisting and vibrational motion of the carboxylate groups and (2) concerted transverse vibration of the linear linkers. The vibrational mechanism is confirmed by calculations of the dynamics in a molecular fragment of the framework.

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Non-local, density functional calculations have been performed on the ground and low-lying excited states of the 3d transition metal monoxide and monosulfide molecules. Periodic trends in properties such as bond lengths, bond energies, vibrational force constants and dipole moments are examined. The variations in the bond lengths and energies are compared to those in the monoxide and monosulfide solids. Analysis of the electronic structures of the molecules reveals the d-orbital splitting to be d π > d σ ≥ d δ and shows the non-bonding role of the d σ and d δ functions. This sequence mirrors that in the electronically related dichloride molecules and leads to similarities in the periodic trends with those in the solids. Systematic errors in the calculated bond energies and vibrational frequencies are identified. The successful use of a correctly parameterized ligand-field model is reported allowing quantitative reproduction of the 'd-d' spectra of the monoxide molecules and the prediction of band positions of unassigned transitions. Computational detailsAll density functional calculations were performed here using the 'DeFT' code written by St-Amant 14 in the linear combination of Gaussian-type orbitals (LCGTO) framework. All the reported calculations used the Vosko-Wilk-Nusair 15 local spin density (LSD) approximation of the correlation part of the exchange-correlation potential with non-local corrections using the Becke 16 functional for exchange and the Perdew 17 func-Paper a906523g

As part of curriculum renewal in three introductory chemistry courses at the University of Sydney, a partially flipped classroom model was implemented. Content that is conventionally delivered in the in-class sessions was moved online through the use of web-based tutorials, videos and quizzes. During the in-class sessions, active learning opportunities were created through the use of guided-inquiry worksheets and clicker responses to promote peer-to-peer and instructor-led discussion. Student evaluations indicated high satisfaction with the quality of teaching and learning resources. In comparison to before these changes, there was a statistically significant increase in the number of students achieving the higher grades across all three courses. Failure rates decreased or remained unchanged. The course with the historically highest attrition showed a marked improvement. These results suggest that a partially flipped learning model can be useful to enhance student engagement, support learning, and positively impact on retention and academic performance.

Abstract. Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data -one that puts the relationship between teacher and student at the center. Our approach means that teachers' pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.Keywords: actionable intelligence, implementation, intelligence amplification, learning analytics, personalization, student engagement. Abbreviations EDMEducational data mining EWS Early warning system LA Learning analytics LMS Learning management system SRES Student Relationship Engagement System 2 Introduction The state of data-driven student supportThe rise in use of technology mediation in learning scenarios is providing unprecedented amounts of data about how educational institutions work and how students participate in learning experiences. At the same time, learning scenarios are becoming increasingly diverse and complex. The areas of educational data mining (EDM) and learning analytics (LA) have emerged to address the issue of how to use data to improve our understanding of learning, and enhance the overall quality of the learning experience for the student. Although EDM and LA researchers and practitioners maintain a similar focus (Baker and Siemens 2014), they differ in their approach to data generated in educational settings. Researchers in EDM frequently focus their analyses on the formulation or improvement of data mining algorithms designed to detect and predict important factors in a learning scenario. LA, on the other hand, focuses on how these algorithms can be deployed and integrated in learning designs, used by teachers, and provide tangible improvements for students. However, in their initial stages, both disciplines placed their emphasis mostly on how data can be collected and used by algorithms and not so much on how these data can then lead to actions that have a positive effect on students.Prior to the availability of massive amounts of data, the area...