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Professional development (PD) is a key element for enhancing the quality of academic teaching. An increasing number of PD activities have moved to blended and online formats, especially since the COVID-19 pandemic. Due to the desire, potential, and need for collaboration among educators to learn from innovative and best practices, several institutions have started to pool their resources and expertise together and have started to implement cross-institutional and cross-national online professional development (OPD). The questions of what type of a (cross-)institutional OPD educators might prefer, and whether educators learn effectively from (and with) peers in such cross-cultural context have not been adequately explored empirically. In this case-study across three European countries, we explored the lived experiences of 86 educators as a result of a cross-institutional OPD. Using a mixed methods design approach our pre-post findings indicated that, on average, participants made substantial gains in knowledge. In addition, several cultural differences were evident in the expectations and lived experiences in ODP, as well as the intention to transfer what had been learned into one's own practice of action. This study indicates that while substantial economic and pedagogical affordances are provided with cross-institutional OPD, cultural differences in context might impact the extent to which educators implement lessons learned from OPD.

To ensure the validity of an assessment programme, it is essential to align it with the intended learning outcomes (LO). We present a model for ensuring assessment validity which supports this constructive alignment and uses learning analytics (LA). The model is based on LA that include a comparison between ideal LO weights (expressing the prioritization of LOs), actual assessment weights (maximum assessment points per LO), and student assessment results (actually obtained assessment points per LO), as well as clustering and trace data analysis. These analytics are part of a continuous improvement cycle, including strategic planning and learning design (LD) supported by LO prioritization, and monitoring and evaluation supported by LA. To illustrate and test the model, we conducted a study on the example of a graduate-level higher education course in applied mathematics, by analysing student assessment results and activity in a learning management system. The study showed that the analyses provided valuable insights with practical implications for the development of sound LD, tailored educational interventions, databases of assessment tasks, recommendation systems, and self-regulated learning. Future research should investigate the possibilities for automation of such LA, to enable full exploitation of their potential and use in everyday teaching and learning.

Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic performance, but the question is how significant its predictive power is, and what other elements can affect predictions. Objectives: Our aim was to investigate the predictive power of formative assessment for summative assessment, measuring the acquisition of LOs. Methods: We analysed formative assessment results (quizzes, homework), together with log data (video and other material use, class attendance), to determine the most influential predictors and establish a reliable predictive learning analytics model. We used the Random Forest algorithm. The model is based on the data from two university mathematical courses, delivered at different years and levels of study, incorporating 813 students in two consecutive years. Results and Conclusions: Our results show that formative assessment, together with previous summative assessment, is a stronger predictor of summative assessment results than other data on students' engagement. The study pointed to the importance of completeness and quality of data, and clear links between assessment and LOs when making predictions of student results. It suggested that predictions are less reliable for the lowest and the highest performing students. It was noted that other factors can also affect predictions, like the level of LOs, or factors not easily extracted from digital data, like the learning environment and individual students' strategies. Lay Description: What is currently known about this topic?: When educators design lessons, they aim for students to acquire intended learning outcomes.To know if students are making progress, educators use a mix of regular checks, like quizzes (formative assessment) and major exams (summative assessment).Predictive learning analytics may contribute to sound learning design (LD). What does this paper add?: In our study, we looked into whether these regular checks can predict how a student might perform on high‐stake exams.We collected data from students taking two university maths courses, including their scores on quizzes, their attendance, and their use of study materials.Using a machine learning technique called the Random Forest algorithm, we built a model to predict student exam scores.Our results indicate that formative assessment is the best predictor of the success in summative assessment. Implications for practice/or policy: We found that how a student performs on regular checks is a good sign of how they might do on the high‐stake exam and careful preparation of assessment and whole LD help student learning.Assessment, formative as well as summative, needs to be aligned with intended learning outcomes and only in this case results can inform quality assurance processes and the restructuring of LD.There are other personal factors that can influence a student's performance, which are not easy to spot just from the digital data we have.The complexity of what is being taught plays a role in predicting students' final scores. [ABSTRACT FROM AUTHOR]