A two-step clustering approach for improving educational process model discovery
| Autor: | Awatef Hicheur Cairns, Nasser Khelifa, Kamel Barkaoui, Jacky Akoka, Hanane Ariouat |
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| Přispěvatelé: | ALTRAN (FRANCE), Conservatoire National des Arts et Métiers - CNAM (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Centre d'Etude et De Recherche en Informatique du Cnam - CEDRIC (Paris, France), Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Institut National Polytechnique de Toulouse - INPT (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Fuzzy clustering
Process modeling Computer science Process mining 02 engineering and technology Système multi-agents Machine learning computer.software_genre Educational data mining Electronic mail Clustering Data modeling Business process discovery [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems Fitness 0202 electrical engineering electronic engineering information engineering Cluster analysis business.industry Educational process mining 4. Education Process discovery Modélisation et simulation [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Apprentissage [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | WETICE Proceedings of WETICE 2016 25th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2016) 25th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2016), Jun 2016, Paris, France. pp. 38-43, ⟨10.1109/WETICE.2016.18⟩ |
| DOI: | 10.1109/WETICE.2016.18⟩ |
| Popis: | International audience; Process mining refers to the extraction of process models from event logs. As real-life processes tend to be less structured and more flexible, clustering techniques are used to divide traces into clusters, such that similar types of behavior are grouped in the cluster. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to better understand students' learning habits and the factors influencing their performance. However, the obtained models, usually, cannot fit well to the general students' behaviour and can be too large and complex for use or analysis by an instructor. These models are called spaghetti models. In the present work, we propose to use a two steps-based approach of clustering to improve educational process mining. The first step consist of creating clusters based employability indicators and the second step consist on clustering the obtained clusters using the AXOR algorithm which is based on traces profiles in order to refine the obtained results from the first step. We have experimented this approach using the tool ProM Framework and we have found that this approach optimizes at the same time, both the performance/suitability and comprehensibility/size of the obtained model. |
| Databáze: | OpenAIRE |
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