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EDUCATIONAL MEASUREMENT IN ITALIAN UNIVERSITIES and THE UNIVERSITY RATING EVOLUTION

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

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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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