This paper introduces a tool for auto-generating criteria-oriented feedback in higher education. A model is trained based on 39 historical student submissions and teacher ratings. The tool combines natural language processing with large language model capacities to analyze contextual criteria, for which regression trees are trained. It aims to mimic teachers when providing feedback utilizing analytic rubrics. The evaluation results with 38 new submissions reveal that predictions are good, serving as a fruitful base to optimize the process when providing formative feedback.

Writing and collaboration are crucial skills in professional and academic settings. However, assessments of collaborative writing often focus only on the final text, overlooking individual contributions and the diverse strategies students employ during the writing process. To support teachers in interpreting and assessing student behaviour, we propose analysing writing data at a granular level, down to individual characters, and using user sessions as observation units to capture coherent interaction patterns. This paper introduces three methods for identifying interaction patterns in collaborative writing. The first method classifies session types by analysing features such as writing, reading, and communication behaviours, session length, and the number of group members collaborating synchronously. The second method identifies frequent sequences of session types by examining their order, enabling process analyses at an abstract yet manageable level compared to using log data. The third method focuses on text-level collaboration by evaluating the frequency of text passage modifications made by the original author or other group members. This approach quantifies individual collaboration and, at the group level, identifies isolated versus closely connected group members, shedding light on the mode of collaboration and degree of group cohesion. We demonstrate these three methods in a case study involving two cohorts, K A = 294 and K B = 242 groups of up to 9 learners (N A = 1, 848, N B = 1, 463). The interaction patterns identified using these methods are intended to help teachers understand collaborative writing processes and identify situations where the participating learners require support.

Computer-generated feedback can be created manifold. This paper compares two approaches for generating feedback: rule-based and prediction-based. Both approaches have several advantages and disadvantages, which are discussed in detail considering precision, recall, human effort for model creation, and explainability requirements.

At present, universities collect study-related data about their students. This information can be used to support students at risk of failing their studies. At the Faculty of Mechanical Engineering (FME), Czech Technical University in Prague (CTU), the group of the first-year students is the most vulnerable. The most critical part of the first year is the winter exam period when students usually divide into those who will pass and fail. One of the most important abilities, students need to learn, is exam planning, and our research aims at the exploration of the exam strategies of successful students. These strategies can be used for improving first-year students retention. The outgoing research on the analysis of exam strategies of the first-year students in the academic year 2017/2018 is reported. From a total of 361 first-year students, successful students have been selected. The successful student is the one who finished all three mandatory exams before the end of the first exam period. From the exam sequences of 153 selected students, a "layered" Markov chain probabilistic model has been constructed. It uncovered the most common exam strategies taken by those students.

This workshop explores how data literacy impacts on learning analytics both for practitioners and for end users. The term data literacy is used to broadly describe the set of abilities around the use of data as part of everyday thinking and reasoning for solving real-world problems. It is a skill required both by learning analytics practitioners to derive actionable insights from data and by the intended end users, such that it affects their ability to accurately interpret and critique presented analysis of data. The latter is particularly important, since learning analytics outcomes can be targeted at a wide range of end users, some of whom will be young students and many of whom are not data specialists.