The current findings provide further evidence for the importance of a family-focused approach to intervention that acknowledges and provides support that is tailored to the unique needs of each individual family. The practical implications of these results as well as directions for future research are discussed.

The substantial focus of resilience research on childhood well-being has resulted in limited knowledge regarding other aspects of resilience in families, such as that of parents. Informed by literature in childhood and family resilience, in this review, we progress conceptual understanding by focusing on parental resilience. The definition of parental resilience, as the capacity of parents to deliver a competent and quality level of parenting to children despite the presence of risk factors, is offered here as a worthwhile framework through which to explore variables thought to contribute to resilience among parents. A conceptual model is proposed whereby parental psychological well-being and self-efficacy, family functioning, and social connectedness are specifically addressed, with each posited as playing an important role in parents' ability to deliver high-quality parenting. In addition to these factors, how parents accommodate adversity and find meaning in their everyday lives within their families is hypothesised to be an important process in understanding parental resilience.

Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.

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