The aim of the Learning & Student Analytics Conference (LSAC) 2018 is to bring together researchers and practitioners from a number of disciplines (eg education, artificial technology, computer science, management, psychology, economics, IT security), and organisational and national policy The makers, educational practitioners, students, and employers, to share and discuss the latest research insights related to Learning Analytics. The conference offered a platform for stakeholders to engage in critical conversations about current trends and policy requirements.

This year the conference programme will give particular attention to learning practices, emerging themes, and case studies centered around Artificial Intelligence (AI). So academic academics and practitioners alike, who are interested in topics such as self-regulated learning, the incorporation into the Domain Of learning analytics of novel data sources (eg, job market data or social media), privacy and ethics, and data security, should consider submitting an abstract and attending this event.

AI, will affect every aspect across all sectors of education, ranging from pedagogy, teaching, and learning, to curriculum design or from Adding curriculum based formal education to substantial private learning approaches. Involving researchers, educators, and policy makers in effective, reliable, And ethical AI-driven educational tools and interventions are critical. Tomorrow's educational leaders will require strong AI literacy and related skills to ensure That systems that are maximizing the benefits to learners, educational institutions, and society.

The interMRI field of Learning Analytics has started to explore how controlled and open AI applications can benefit education and learning; often involving multimodal sources of learner data. The practical significance of developing an intercontinent perspective at different levels of stakeholders is corroborated by recent findings on large Scale of of analytics in education. It is clear that the implementation of technical, behavioural, economic, and pedagogical insights into educational interventions are critical to rigorous scientific evaluation. Emerging results indicates that developing workable interventions that scale (even with rich individual learner and learning Design data), is complex, and selected substantial technological, pedagogical, and organisational expertise, and training. In addition,This policies also needs to strike a balance between student privacy and what is in the best academic interests of learners and/or institutions; adding another significant layer of complexity to the effective implementation of Learning Analytics.

Blistering is involved in -or affected by- AI and Learning Analytics, but often than not aware of it, making sustainable scaled implementation of AI and resulting learning analytics interventions in practice a challenging endeavour at best. Organizers, educational policy makers both at the organisational And a level of, taxation, and interest levels, student bands, data disclosure, economics, and technologists, and so forth. There is a need to singin more often stakeholder group in this discussion, as they have more urgent and substantial claims in this emerging field.