Self-assessment, in the education framework, is a methodology that motivates students to play an active role in reviewing their performance. It is defined as “the evaluation or judgment of ‘the worth’ of one’s performance and the identification of one’s strengths and weaknesses with a view to improving one’s learning outcomes”. The goal of this research is to study the relationship between self-assessment and the development and improvement of problem-solving skills in Mathematics. In particular, the investigation focuses on how accurate the students’ self-evaluations are when compared to external ones, and if (and how) the accuracy in self-assessment changed among the various processes involved in the problem-solving activity. Participants are grade 11 students (in all 182 participants) in school year 2020/2021 who were asked to solve 8 real-world mathematical problems using an Advanced Computing Environment (ACE). Each problem was assessed by a tutor and self-assessed by students themselves, according to a shared rubric with five indicators: Comprehension of the problematic situation, identification of the solving strategy, development of the solving process, argumentation of the chosen strategy, and appropriate and effective use of the ACE. Through a quantitative analysis, students’ self-assessment and tutors’ assessment were compared; data were cross-checked with students’ answers to a questionnaire. The results show a general correlation between tutor assessment and self-assessment, with a tendency of students to underestimate their performance. Moreover, students were more precise in self-assessing in the indicators: Development of the solving process and use of the ACE, while they had major difficulties in self-assessment for the indicators: Comprehension of the problematic situation and argumentation.

Inclusive computational practices are increasingly being employed to enrich knowledge and facilitate sensemaking in STEM education. Embedding computational activities in Computer-Supported Collaborative Learning environments can enhance students’ experiences. This study aimed to investigate the knowledge co-construction process within tailored student-led computational lab activities designed for a Computational Finance module. In particular, this study focused on the analysis of the effects of different lab practices and of group composition on knowledge co-construction. The groups designed for the lab activities were internally homogenous in terms of student ability. The sample consisted of 396 answers to a weekly survey filled out by all 50 of the undergraduate students who attended the module during the AY 2020/2021. The qualitative analysis relied on an adapted version of the Interaction Analysis Model designed by Gunawardena and colleagues for collaborative knowledge construction. Quantitative analyses were then conducted to study how the different lab practices and the composition of the groups affected the interaction. The findings revealed that, although the lower phases were the most prevalent, significant negotiations of meaning and discussions were activated, especially in tasks guiding towards sensemaking. Furthermore, the groups composed of lower-achieving students were the most engaged in negotiating and improving understanding as a result of the group interaction.

In recent decades, Mathematics Education has been deeply transformed to leverage on emerging technologies and to integrate computational thinking in the learning process. In this scenario, computational thinking activities can be further enhanced through collaborative learning approaches. This research study investigates the connections between collaborative knowledge construction and inclusive computational practices in the area of Financial Mathematics. The context of the research is the Advanced Computational Finance module in University College Dublin, Ireland, in Spring 2022, and in particular its lab activities, which were re-designed starting from previous years offerings to further facilitate the activation of collaborative computational practices. It was attended by 22 students, who worked in groups on the proposed lab activities. Students were assigned to heterogeneous groups based on grade point average. We analyzed 176 responses to a weekly survey that the students completed after each weekly lab. We performed a qualitative analysis of the answers using an adapted version of the Interaction Analysis Model and compared the results with the sample related to the previous year, where the practices activated were different. We then explored how redesigned lab activities influenced collaborative knowledge construction. Statistical tests allowed us to validate the model used for the qualitative analysis and to identify the computational practices, which mainly facilitated knowledge co-construction.

Collaborative learning is an engaging methodology to captivate students in problem-solving activities and inclusive computational lab practices and to foster sensemaking. The knowledge co-construction process can be influenced by group composition. This study aims to investigate how group composition affects knowledge co-construction in student-led computational lab activities in Financial Mathematics. This was done by comparing two different group compositions working within a Computer-Supported Collaborative Learning environment, in two consecutive academic years, namely AY 2020/2021 with internally homogeneous ability groups and AY 2021/2022 with internally heterogeneous ability groups. 572 student responses to a weekly survey were included in the analysis and the adapted Interaction Analysis Model was used to investigate peer interactions.

This study investigates the development of intercultural competence through a Collaborative Online International Learning (COIL) project focused on Linear Algebra, involving undergraduate students from University College Dublin and the University of Turin. The research aims to identify which dimensions of Cultural Intelligence are impacted by these mathematical activities and whether different Mathematics educational backgrounds, based on the country's schooling system, influence students’ Cultural Intelligence. Qualitative analysis of student reflections and statistical testing reveal that nearly all participants gained significant levels of cultural knowledge, while cross-cultural skills and cultural metacognition emerged less frequently. No statistically significant differences were found between institutions, indicating that the COIL experience had a positive impact regardless of the national curricula or the specific mathematical cultural backgrounds of the participants. The study concludes that short-term COIL interventions can foster foundational development in Cultural Intelligence, providing a necessary basis for the development of intercultural sensitivity.