Learning analytics sits in the middle space between learning theory and data analytics. The inherent diversity of learning analytics manifests itself in an epistemology that strikes a balance between positivism and interpretivism, and knowledge that is sourced from theory and practice. In this paper, we argue that validation approaches for learning analytics systems should be cognisant of these diverse foundations. Through a systematic review of learning analytics validation research, we find that there is currently an over-reliance on positivistic validity criteria. Researchers tend to ignore interpretivistic criteria such as trustworthiness and authenticity. In the 38 papers we analysed, researchers covered positivistic validity criteria 221 times, whereas interpretivistic criteria were mentioned 37 times. We motivate that learning analytics can only move forward with holistic validation strategies that incorporate “thick descriptions” of educational experiences. We conclude by outlining a planned validation study using argument-based validation, which we believe will yield meaningful insights by considering a diverse spectrum of validity criteria.

Cybersecurity incidents are commonplace nowadays, and Small- and Medium-Sized Enterprises (SMEs) are exceptionally vulnerable targets. The lack of cybersecurity resources available to SMEs implies that they are less capable of dealing with cyber-attacks. Motivation to improve cybersecurity is often low, as the prerequisite knowledge and awareness to drive motivation is generally absent at SMEs. A solution that aims to help SMEs manage their cybersecurity risks should therefore not only offer a correct assessment but should also motivate SME users. From Self-Determination Theory (SDT), we know that by promoting perceived autonomy, competence, and relatedness, people can be motivated to take action. In this paper, we explain how a threat-based cybersecurity risk assessment approach can help to address the needs outlined in SDT. We propose such an approach for SMEs and outline the data requirements that facilitate automation. We present a practical application covering various user interfaces, showing how our threat-based cybersecurity risk assessment approach turns SME data into prioritised, actionable recommendations.

In a world where cybersecurity has an increasing importance, any company, regardless of what sector, size or activity is related to, should rely on tools and solutions that can help it to be secure in the best possible way. The GEIGER platform described in this paper acts as a perfect fit for micro and small enterprises (MSEs). These companies need to be protected against threats but sometimes do not have the resources (money, personnel, time…) to deal with them. Often, private cybersecurity solutions are either expensive or hard to implement for micro and small companies. However, GEIGER is designed to bring cybersecurity principles, security countermeasures and awareness in a smooth and friendly way, with special focus on the MSEs. Its ability to adapt to new challenges comes in handy when dealing with sophisticated threats and the functionalities provided to help MSEs adopting a more prominent security posture. Having the support of an innovative solution can help MSEs to achieve a more effective approach regarding cybersecurity, which leads to a better overall business management and operation.