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[99380] Artykuł:

Interactive Planning of Competency-Driven University Teaching Staff Allocation

Czasopismo: Applied Sciences Emerging Artificial Intelligence (AI) Technologies for Learning   Tom: 10, Zeszyt: 14
ISSN:  2076-3417
Wydawca:  MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
Opublikowano: 2020
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3
Grupa
przynależności
Dyscyplina
naukowa
Procent
udziału
Liczba
punktów
do oceny pracownika
Liczba
punktów wg
kryteriów ewaluacji
Eryk Szwarc Niespoza "N" jednostki20.00.00  
Jarosław Wikarek orcid logo WEAiIKatedra Systemów Informatycznych *Takzaliczony do "N"Automatyka, elektronika, elektrotechnika i technologie kosmiczne20100.00100.00  
Arkadiusz Gola Niespoza "N" jednostki20.00.00  
Grzegorz Bocewicz Niespoza "N" jednostki20.00.00  
Zbigniew Banaszak Niespoza "N" jednostki20.00.00  

Grupa MNiSW:  Publikacja w czasopismach wymienionych w wykazie ministra MNiSzW (część A)
Punkty MNiSW: 100
Klasyfikacja Web of Science: Article


DOI LogoDOI     Web of Science Logo Web of Science    
Keywords:

interactive planning  competency framework  teacher assignment  robustness  



Abstract:

This paper focuses on a teacher allocation problem that is specifically concerned with assigning available academic lecturers to remaining courses from a given student curriculum. The teachers are linked to tasks according to competencies, competence requirements enforced by the curriculum as well as the number and type of disruptions that hamper the fulfilment of courses. The problem under consideration boils down to searching links between competencies possessed by teachers and competencies required by the curricula that will, firstly, balance student needs and teacher workload and, secondly, ensure an assumed robustness level of the teaching schedule. The implemented interactive method performs iterative solving of analysis and synthesis problems concerned with alternative evaluation/robustness of the competency framework. Its performance is evaluated against a set of real historical data and arbitrarily selected sets of disruptions. The computational results indicate that our method yields better solutions compared to the manual allocation by the university.