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The purpose of this paper is to outline a hierarchical model for developing essential e-textbook functionality and to offer important insight for e-textbook designers.

Guided by the SAMR model, five hierarchical functional layers are proposed in the development process for e-textbook to enable more effective and efficient teaching and learning.

Moving up from the substitution to transformation level, each layer extends the functionality of the underlying layers to unveil new features that support the learning processes and gradually transform e-textbooks from a self-directed informal learning tool to become a formal learning platform.

The hierarchical model provides e-textbook designers and developers with a guideline on how to surpass the present state of e-textbooks and offer additional value-added features for learners. The required technologies for each layer are also discussed.

This paper aims to review and identify the major patterns and trends in learning analytics practices in higher education institutions. The review covers the characteristics of the institutions, as well as the characteristics and outcomes of the learning analytics practices.

This research collected literature published in 2011-2016 which reported learning analytics practices from Scopus and Google Scholar, covering a total of 47 institutions, and categorised the information about the relevant institutions and practices.

The results show that most of the institutions were public ones in the USA and the UK of various sizes and offering different levels of study. The learning analytics practices were mainly institution-wide, apart from a small number focusing on selected courses. The purposes of the practices were mainly to enhance the effectiveness of learning support and administration, followed by facilitating students’ learning progress. The most common types of data collected for the practices were students’ academic behaviours and their background information. Positive outcomes were reported for a majority of the practices, and the most frequent ones being an increase in cost-effectiveness and understanding of students’ learning behaviours. Other outcomes included the improvement of student retention, timely feedback and intervention, support for informed decision-making and the provision of personalised assistance to students.

The results provide an overview of the use of learning analytics in the higher education sector. They also reveal the trends in learning analytics practices, as well as future research directions.