Learning Effectiveness Enhancement Project “LEEP”

Chapter
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

The overall objective, to which the project will contribute, is to improve teaching and learning effectiveness within academic institutions by exploiting data mining methods on collected databases for educational knowledge extraction. These teaching and learning databases are accumulated from quantitative “measures” done through indoor classroom visits within academic institutions, online web access learners’ questionnaires and answers, paper written statements’ analysis of academic exams in STEM education (science, technology, engineering, and mathematics), and online elementary grades seizure from written traces of learners’ performances in STEM exams. Findings of these processes, elaborated by researcher’s team within beneficiary organizations, are disseminated through diversified publication and are the subject of multiple professional meetings, especially, teachers’ training sessions. The project’s data mining strategy in educational context will support and develop teachers’ expertise, enhance and scaffold students’ learning, and improve and raise education system’s performance. This is a project that combines data mining analysis methods with educational and cognitive science findings. It attempts to unify these two paradigms, generally distant from each other. New strategies of educational assessment, training, and innovating are designed and are able to enhance significantly the effectiveness of teaching and learning performances in academic institutions such as secondary schools. The use of these methods aims to identify and better understand the learners’ profiles, teaching practices, characteristics, and context details in which teachers and learners act. These tools for decision support are exploited by the researcher, an educational inspector and expert in educational assessment, to generate, make available, and process databases on teaching practices, learning performances, and learners’ profiles.

Keywords

Blended learning Assessment for learning Knowledge extraction Profile recognition 

Notes

Acknowledgements

I would like to express my deepest appreciation to Professor Mohamed JEMNI, Director of ICT in The Arab League Educational, Cultural and Scientific Organization—ALECSO, who convincingly provided assistance that enhanced the quality of this work.

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Research Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE)TunisTunisia
  2. 2.Ecole Supérieure des Sciences et Techniques de TunisTunisTunisia
  3. 3.MonastirTunisia

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