Kagawa University has been implementing various education and research initiatives based on industry–academia collaboration/co-creation with the aim of “providing educational opportunities with diverse faculty members” and “deepening discussions and understanding between those who develop human resources and those who utilize them”. In this paper, we describe these initiatives implemented in Kagawa University and the knowledge gained through these initiatives.

This paper describes Learning Innovation Grand Prix, one of the events for academic–industrial alliance. Learning Innovation Grand Prix is one of the attempts of mutual understanding to solve the disagreement of understanding which is a big gap in the academic–industrial alliance. In this paper, we introduce the Learning Innovation Grand Prix as an academic–industrial alliance that researchers can start immediately, and describe its establishment and characteristics. We also describe the history of the Learning Innovation Grand Prix, where academia and industry have worked together for mutual understanding.

This commentary article encourages and navigates inexperienced readers to IAC (Industry–Academia Collaboration) in the field of education and learning support systems. It includes a survey of IAC history, major patterns thereof and recent activities in JSiSE. A major pattern of IAC in engineering field is technology transfer from one another but there are other possibilities in this multidisciplinary community, for instance, that is collaborative project for education/learning support practice. In any cases, the community members taking either role in industry or academia should clearly be aware of the necessity and impact on working together. They would be able to play a role in covering both R&D innovations and practical innovations in education and learning.

Recently, automated short-answer grading (ASAG) methods based on deep neural networks (DNN) have attained high scoring accuracy. However, the accuracy requires further improvement especially for large-scale and high-stakes tests because a slight scoring error will strongly influence many examinees. To improve the accuracy, this study proposes a new DNN-based ASAG method that utilizes examinees’ abilities which are estimated using an item response theory model from their true-false responses for objective exam questions offering with a target short-answer question.

This paper describes a design of learning method aiming to promote learners’ acquisition of the analysis viewpoints in system requirements analysis. We provided learners with three viewpoints, “Functional defects”, “Operability” and “Users”. By doing this, we intended to promote learners to point out the problems about system such as lack of functions and ambiguity of requirements. In addition, we tried to make learners re-analyze task to aim that they acquire of relationship of abstract viewpoints and specific problems themselves. As a result of experiment, we found that the learners without work experience improved problem-pointing by being aware of viewpoints.