Elements of descriptive statistics: essential terminology (statistical unit, collective, sample, characters, classification of characters) summary values (mode, median, quartiles, arithmetic mean, arithmetic mean properties), variability (indices of heterogeneity, standard deviation, variance, coefficient of variation) association indices (the chi-square), correlation indices (Bravais-Pearson linear correlation coefficient), linear dependence analysis
Use of the main Excel statistical functions: count.if, pivot tables, function for calculating the mode, function for calculating the median and quartiles, function for calculating the arithmetic mean, function for calculating the standard deviation, function for calculating the variance, function for calculating the correlation, function for calculating the regression line and the index of determination.

1. Knowledge-based economy, institutions and economic development
1.1. Knowledge and globalization
1.2. Knowledge based economies and educational systems
1.3. Knowledge and training
1.4. Knowledge and evaluation
2. Origins and developments of evaluation research in Pedagogy
2.1. Measurement of knowledge and its evaluation
2.1.1. History of evaluative research in Pedagogy
2.1.2. International Association for Evaluation of Educational
Achievement (IEA)
3. The European framework for evaluative research
3.1. Europe 2030: objectives of education in Europe
3.2. The international indicators of evaluation
3.3. Education and Evaluation Systems in Europe
3.4 School autonomy and system evaluation in Italy
4. Holistic approach to the person: the assessment of skills
4.1 Indications for the school curriculum
4.2 Evaluate for skills
4.3 Skills in the school curriculum
4.4 Measurement scales in school evaluation
4.5 Qualitative methods of evaluation
4.6 The methods of quantitative evaluation
4.6.1 The objective evaluation tests
4.6.2 Types of structured tests
4.6.3 The construction of structured verification tests
4.7 School evaluation of learning: tradition and innovation in Italy
4.8 Standardization of measurements and normal distribution
4.9 The standard normal distribution.
5. Classical Test Theory (CTT) - Item Response Theory (IRT)
5.1 Classical Test Theory (CTT): basic principles and assumptions
5.1.1 Analysis of the answers to a questionnaire
5.1.2 Analysis of the reliability of a test
5.1.3 Analysis of the validity of a test
5.1.4 Measurement of learning outcomes and correction for guessing
5.1.5 Analysis of a test according to the CTT: case study
5.2 ItemResponseTheory (IRT)
5.2.1 Model assumptions
5.2.2 The postulates of the Item Response Theory
5.2.3 The Rasch model and the one-parameter logistics model (1PL)
5.2.4 The maximum likelihood (ML) method for model estimates
5.2.5 The information function
5.2.6 The two-parameter logistics model (2PL)
5.2.7 The three and four parameter logistics model
5.2.8 Case study
5.3 Classical Test Theory (CTT) and Item Response Theory (IRT): models
compared
6. Evaluation in Special Education
6.1 Special educational needs
6.3 Computer Adaptive Testing (CAT)
7. INVALSI tests