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Academic Year 2025/2026 - Teacher: FRANCESCO MAZZEO RINALDI

Expected Learning Outcomes

Expected learning outcomes

Knowledge and understanding: acquire knowledge of the main approaches and models of evaluative research, including the most recent developments related to the use of digital tools and data analytics.

Application skills: apply evaluation techniques to concrete cases, including through the use of software and digital tools for data analysis.

Judgment and independence: develop critical skills in selecting the most appropriate evaluation models and interpreting results.

Communication skills: present analyses and evaluation reports using scientific language and digital visual tools.

Learning skills: acquire independence in studying the literature and experimenting with digital tools for evaluation.

Course Structure

In addition to lectures and online teaching: practical exercises, analysis of real datasets, simulations with evaluation software, thematic seminars on digital innovations in evaluation.

Required Prerequisites

Basic knowledge: social research methodology

Attendance of Lessons

Highly recommended for empirical applications, for the proposed case studies, and to access ongoing assessments.

Detailed Course Content

Approaches and models of evaluation research.

Theory-based evaluation, realist explanation, and causal inference.

Statistical and evaluation indicators.

Monitoring for evaluation.

Digital innovations in evaluation: use of BD and AI

Textbook Information

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· Bezzi, C., Cannavò L., Palumbo M. (2010) Costruire indicatori nella Ricerca Sociale e nella Valutazione, Milano, FrancoAngeli: pp. 19-56.

· Stame N., (2016) Valutazione pluralista. Milano, Franco Angeli, pp 23-111.

· Stern E. (2016) La valutazione di impatto. Una guida per committenti e manager preparata per Bond. Milano, Franco Angeli, pp 13-65.

· Mazzeo Rinaldi F., (2012) Il monitoraggio per la valutazione, Milano, FrancoAngeli: pp 17-43 pp 67-115.

· Stame N. - a cura - (2007) Classici della valutazione. Milano, Franco Angeli, pp. 337-416.

· Mazzeo Rinaldi F. (2018) Big Data e Valutazione: una relazione ancora da costruire. RIV Rassegna Italiana di Valutazione. XXI (68). FrancoAngeli, Milano. pp. 83-100.

· Mazzeo Rinaldi F., Occhipinti O. (2023) Big Data, Intelligenza Artificiale e Valutazione: cosa accade in Italia. RIV Rassegna Italiana di Valutazione. XXVII (85-86),. FrancoAngeli, Milano. pp.185-207

Learning Assessment

Learning Assessment Procedures

In addition to the written exam: project work based on datasets or case studies, oral presentations of results, practical exercises with digital tools.

Examples of frequently asked questions and / or exercises

Questions about program content; Differences between evaluation approaches; Causal inference; Impact assessment; The relationship between monitoring and evaluation; Opportunities and limitations of digital innovations in evaluation research

Degree Course in

Sociology of networks, information and innovation