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The purpose of this paper is to promote decision-making structures between the customer and the supplier in a highly uncertain environment. This phenomenon of demand-supply chain synchronisation includes sharing of high-quality and timely demand and supply information in order to improve the quality and speed of decision-making.

The study was carried out as an abductive case study, which started from empirical observations that did not match the prior theoretical framework. Through abductive reasoning and empirical experiments, the prior framework was extended to a new synchronisation model and tools that better accommodate the observed need.

A new co-innovation toolbox was developed to create common understanding of demand-supply chain synchronisation between the customer and the supplier. The toolbox includes Demand Visibility Point-Demand Penetration Point, Supply Visibility Point–Supply Penetration Point and Integrative Synchronisation tools.

The study extends the current models and tools of demand-supply chain synchronisation. With the new toolbox, the development needs of decision-making structures can be identified more comprehensively than with the current tools.

The developed visual toolbox helps partners create a common understanding of problems and development possibilities in demand-supply chain synchronisation in a highly uncertain environment. Common understanding is a starting point for changing decision-making structures to improve the overall performance of a demand-supply chain.

The new toolbox is both more comprehensive and more detailed than the previous tools.

Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland.

The available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students’ learning behaviour.

It was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes.

The research was limited by the domain of available variables, which is a common limitation in learning analytics research.

The practical implications include important ideas for future research and interventions in digital learning.

The importance of research on soft skills, social skills and collaboration is highlighted.

The paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research.