Identifying Great Teachers Through Their Online Presence

  • Evanthia Faliagka
  • Maria Rigou
  • Spiros Sirmakessis
Conference paper

DOI: 10.1007/978-3-319-46963-8_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9881)
Cite this paper as:
Faliagka E., Rigou M., Sirmakessis S. (2016) Identifying Great Teachers Through Their Online Presence. In: Casteleyn S., Dolog P., Pautasso C. (eds) Current Trends in Web Engineering. ICWE 2016. Lecture Notes in Computer Science, vol 9881. Springer, Cham

Abstract

Evaluating candidate teachers is a very tricky task, as there are a lot of criteria -objective and not- that are important for identifying a good teacher. The teacher’s efficiency depends on the academic qualifications and experience, on teacher’s personality, even the students of the class and how well teaching and learning dynamically ‘grows’. In this work we propose a novel approach for teacher online evaluation. We implemented a prototype system which extracts values for a set of objective criteria from the teachers’ LinkedIn profile, and infers personality characteristics using linguistic analysis on their Facebook and Twitter posts. Machine learning algorithms were used to solve the final ranking problem.

Keywords

E-recruitment systems Personality mining Personality traits Social web mining Recommendation systems Teacher evaluation 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Evanthia Faliagka
    • 1
  • Maria Rigou
    • 2
    • 3
  • Spiros Sirmakessis
    • 1
    • 3
  1. 1.Department of Computer and Informatics EngineeringTechnological Institution of Western GreeceAntirrioGreece
  2. 2.Department of Computer Engineering and InformaticsUniversity of PatrasPatrasGreece
  3. 3.Hellenic Open UniversityPatrasGreece

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