The COVID-19 pandemic has evidenced a need for tools and methodologies to support students’ autonomous learning and the formative assessment practices in distance education contexts, especially for students from challenging backgrounds. This paper proposes a conceptualization of Interactive Feedback (IF) for Mathematics, which is a step-by-step interactive process that guides the learner in the resolution of a task after one or more autonomous tentative. This conceptualization is grounded on theories and models of automatic assessment, formative assessment, and feedback. We discuss the effectiveness of the IF for engaging students from low socio-economic contexts in closing the gap between current and reference performance through a didactic experimentation involving 299 Italian students in grade 8. Using quantitative analyses on data from the automatic assessment, we compared the results of the first and last attempts in activities with and without IF, based on algorithmic parameters so that the task changes at every attempt. We found that IF was more effective than other kinds of activities to engage learners in actions aimed at improving their results, and the effects are stronger in low socio-economic contexts.

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.

This paper will analyze an educational model for automated formative assessment developed at the Department of Mathematics of University of Turin for learning and teaching Mathematics and scientific disciplines. The model is provided through an automated grading system which, empowered by the engine of an advanced computing environment, allows the creation of algorithmic variables and open mathematical answers, recognized in all their equivalent forms.The adoption of automated formative assessment brings many advantages to learning. Easily available assignments, immediate feedback, adaptivity, the chance of learning from mistakes turn assessment into a fundamental enhancement in education; the intrinsic “rigidity” of technology can also have positive results on students’ path to knowledge. Automated assessment brings innovation into teaching: time saved in grading can be used to improve materials and activities, teachers easily get information about students’ learning, they need to change their approach and to attend trainings; sharing and collaboration among teachers are facilitated.Results obtained by the application of automated formative assessment in several class experiences are discussed and data about emerged satisfaction and criticisms are shown

Formative assessment is a practice aimed to enhance teaching and learning and to develop self-regulation. Among the strategies for activating formative assessment, the provision of feedback is one of the most studied: good feedback helps close the gap between actual and desired performance and it promotes self-regulation. Automatic Assessment Systems can improve the practice of formative assessment, especially for the immediacy of feedback and the flexible solutions for questioning. In this context, the Department of Mathematics of the University of Turin has developed a model for automatic formative assessment using an automatic assessment system suitable for STEM disciplines. The key features of the model, grounded on the models from the literature, are: availability, algorithmic questions, open answers, immediate feedback, interactive feedback, and contextualization. These are detailed and discussed through evidence gained from three experimentations, involving the total number of 553 students. In particular, it emerges that the use of interactive feedback ensures that students process the information from the feedback and use it to improve their performance, thus solving the major problem raised in the literature that feedback are not useful when students do not read them.

Assessment of learning and assessment for learning are at the core of the research on new teaching strategies involving the use of new technologies recently performed by University of Turin. The practice of automated assessment in Mathematics using the grading system Maple T.A. Has been introduced in many undergraduate scientific courses and, after the initial success, it has been diffused in high-schools through several projects aimed to improve Maths teaching and learning. The following paper is intended to describe the effectiveness of automated assessment as a learning tool, the strength of Maple T.A. For grading Mathematics and its integration in a learning content management system, and the results obtained at University and in high-schools.