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The purpose of this paper is to report the design and evaluation of an inter-university collaborative project entitled “Blended learning for building student-teachers’ capacity to learn and teach science-related interdisciplinary subjects.” The project is a response of the science education faculty of three Hong Kong tertiary institutes to the challenge of catering to the diversity of academic backgrounds among student-teachers.

E-learning modules have been produced covering four content domains of science. These modules are designed based on the 5E learning model and are delivered to students using the learning management system provided by Moodle. The design of the modules is iterative, based on the evaluation of three consecutive rounds of trials through student surveys, and focus group interviews with students and course lecturers.

The evaluation findings indicate positive outcomes for certain attributes such as conceptual understanding, eagerness and confidence in learning science, and metacognitive reflection on students’ own learning. There are challenges to be met in relation to instructional design to cater for the diversity of student abilities, and enhance motivation in self-directed learning.

The project indicates the ways to develop students’ basic science knowledge in a mixed-ability setting through the design of self-directed e-learning modules blended with their major courses and possible measures to address the limitations of such design.

The study represents a conscious effort for the science teacher education faculty of different universities to pull together to tackle a perennial teaching and learning problem. The findings provide important insights into possible ways to blend e-learning with face-to-face learning approaches to better cater to the needs of science learners with mixed abilities to prepare them for interdisciplinary teaching.

This study aims to develop an interdisciplinary political theory of data justice by connecting three major political theories of the public good with empirical studies about the functions of big data and offering normative principles for restricting and guiding the state’s data practices from a public good perspective.

Drawing on three major political theories of the public good – the market failure approach, the basic rights approach and the democratic approach – and critical data studies, this study synthesizes existing studies on the promises and perils of big data for public good purposes. The outcome is a conceptual paper that maps philosophical discussions about the conditions under which the state has a legitimate right to collect and use big data for public goods purposes.

This study argues that market failure, basic rights protection and deepening democracy can be normative grounds for justifying the state’s right to data collection and utilization, from the perspective of political theories of the public good. The state’s data practices, however, should be guided by three political principles, namely, the principle of transparency and accountability; the principle of fairness; and the principle of democratic legitimacy. The paper draws on empirical studies and practical examples to explicate these principles.

Bringing together normative political theory and critical data studies, this study contributes to a more philosophically rigorous understanding of how and why big data should be used for public good purposes while discussing the normative boundaries of such data practices.