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EDUCATIONAL MEASUREMENT IN ITALIAN UNIVERSITIES and THE UNIVERSITY RATING EVOLUTION

Explore the history, crisis, and current situation of Italian universities, plus a model for the evolution of university ratings.

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EDUCATIONAL MEASUREMENT IN ITALIAN UNIVERSITIES and THE UNIVERSITY RATING EVOLUTION

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  1. EDUCATIONAL MEASUREMENT IN ITALIAN UNIVERSITIES and THE UNIVERSITY RATING EVOLUTION by Raimondo Manca

  2. SOME HISTORY • In the second half of the 19° century Italy was unified • There was a high rate of illitteralicy • Big differences between the north and south • Two big intuitions at the end of the century • Montessori’s method • The teacher should adequate to the scholars • Creation of universities devoted to the preparation of teachers for primary school • Italian primary school was very good and the Montessori method was adopted in many countries

  3. CRISIS OF ITALIAN SCHOOL • Strong increase of university students in seventies • Many people became University professors without the quality • The school teachers were paid less • Teaching was considered a second best • More and more students that are not well prepared arrive at university • A vicious circle was created

  4. ACTUAL SITUATION • The Faculties that prepared primary school teachers were cancelled and were transformed in something more general (Educational Sciences) • There are 36 faculties of Educational Sciences • 16 statistics or psicometry professors (associate or full) • 9 assistant professors • Educational measurement is not yet well established in Italy

  5. COUNTRIES OF FIRST 100 UNIVERSITIES

  6. FIRST 20 WORLD UNIVERSITY

  7. UNIVERSITY RATING TIME EVOLUTION I • Hypotheses • Homogeneity • Semi-Markov process • Discrete time (DTHSMP) • Steps • State subdivision • Embedded Markov Chain • Waiting time distribution function • Model application • Results

  8. STATE SUBDIVISION • State set: • State 1 1-20 • State 2 21-40 • State 3 41-70 • State 4 71-100 • State 5 101-150 • State 6 151-200 • State 7 201-300 • State 8 301-400 • State 9 401-500 • State 10 No Rating

  9. INPUT CONSTRUCTION • Embedded Markov Chain construction • Number of transitions from the state i to the state j • Number of transition from the state i • Probability to go from state i to state j • Waiting time distribution function • number of transition from i to j within a time t • probability to have a transition in a time ≤ t

  10. HSMP 1

  11. HSMP 2

  12. CONCLUSIONS • Universities • Private universities • Countries • Data collection for the first 6000 world universities • Construction of input data

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