Learning analytics have the potential to improve teaching and learning in K–12 education, but as student data is increasingly being collected and transferred for the purpose of analysis, it is important to take measures that will protect student privacy. A common approach to achieve this goal is the de-identification of the data, meaning the removal of personal details that can reveal student identity. However, as we demonstrate, de-identification alone is not a complete solution. We show how we can discover sensitive information about students by linking de-identified datasets with publicly available school data, using unsupervised machine learning techniques. This underlines that de-identification alone is insufficient if we wish to further learning analytics in K–12 without compromising student privacy.

Teachers and learners who search for learning materials in open educational resources (OER) repositories greatly benefit from feedback and reviews left by peers who have activated these resources in their class. Such feedback can also fuel social-based ranking algorithms and recommendation systems. However, while educational users appreciate the recommendations made by other teachers, they are not highly motivated to provide such feedback by themselves. This situation is common in many consumer applications that rely on users' opinions for personalisation. A possible solution that was successfully applied in several other domains to incentivise active participation is gamification. This paper describes for the first time the application of a comprehensive cuttingedge gamification taxonomy, in a user-centred participatory-design process of a OER system for Physics, PeTeL, used throughout Israel. Physics teachers were first involved in designing gamification features based on their preferences, helping shape the gamification mechanisms likely to enhance their motivation to provide reviews. The results informed directly the implementation of two gamification elements that were implemented in the learning environment, with a second experiment evaluating their actual effect on teachers' behaviour. After a long-term, real-life pilot of two months, teachers' response rate was measured and compared to the prior state. The results showed a statistically significant effect, with 4X increase in the total amount of recommendations per month, even when taking into account the 'Covid-pandemic effect'.

Teachers and learners who search for learning materials inopen educational resources (OER) repositories greatly benefit from feed-back and reviews left by peers who have activated these resources in theirclass. Such feedback can also fuel social-based ranking algorithms andrecommendation systems. However, while educational users appreciatethe recommendations made by other teachers, they are not highly motivated to provide such feedback by themselves. This situation is commonin many consumer applications that rely on users' opinions for personalisation. A possible solution that was successfully applied in several otherdomains to incentivise active participation is gamification. This paperdescribes for the first time the application of a comprehensive cutting-edge gamification taxonomy, in a user-centred participatory-design pro-cess of a OER system for Physics, PeTeL, used throughout Israel. Physicsteachers were first involved in designing gamification features based ontheir preferences, helping shape the gamification mechanisms likely to enhance their motivation to provide reviews. The results informed directlythe implementation of two gamification elements that were implementedin the learning environment, with a second experiment evaluating theiractual effect on teachers' behaviour. After a long-term, real-life pilot oftwo months, teachers' response rate was measured and compared to theprior state. The results showed a statistically significant effect, with 4Xincrease in the total amount of recommendations per month, even whentaking into account the 'Covid-pandemic effect'.

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