Models and Tools for Usage-based e-Learning Documents Reengineering Madjid Sadallah To cite this version: Madjid Sadallah. Models and Tools for Usage-based e-Learning Documents Reengineering. Tech- nology for Human Learning. Université Abderrahmane Mira de Béjaïa (Algérie), 2019. English. tel-02911978 HAL Id: tel-02911978 https://tel.archives-ouvertes.fr/tel-02911978 Submitted on 4 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. République Algérienne Démocratique et Populaire Ministère de l’Enseignement Supérieur et de la Recherche Scientifique Université A.MIRA-BEJAIA Faculté des Sciences Exactes Département d’Informatique THÈSE Présentée par Madjid Sadallah Pour l’obtention du grade de DOCTEUR EN SCIENCES Filière : Informatique Option : Cloud Computing Thème Models and Tools for Usage-based e-Learning Documents Reengineering Soutenue le : 25/04/2019 Devant le Jury composé de : Nom et Prénom Grade Mr Abelkamal TARI Professeur Univ. de Béjaia Président Mr Yannick PRIÉ Professeur Univ. de Nantes Rapporteur Mr Nadjib BADACHE Directeur de Recherche CERIST Examinateur Mr Abdelouaheb ALOUI MCA Univ. de Béjaia Examinateur Mr Azze-Eddine MAREDJ Maitre de Recherche A CERIST Examinateur Mr Benoît ENCELLE MCF Univ. de Lyon1 Invité Année Universitaire : 2018/2019 To my parents, for their constant support and unceasing love To my precious wife Saliha, for her affection, devotion and patience To our wonderful kids Ilyas, Malek and Maya for their loving little hearts To my whole family! Acknowledgements Completion of this doctoral dissertation was possible with the support of several people. I would like to express my sincere gratitude to all of them. First and foremost, I would like to express my sincerest appreciation to Prof. Yan- nick Prié, who gave me the great opportunity to do my Ph.D. under his supervision. I am profoundly grateful for his thoughtful guidance, expert advice, supportive patience, and faithful encouragement, all along the journey. His intellectual rigor and integral view on research has made a deep impression on me and was my major source of inspiration. I was fortunate to receive the unconditional, sustained and precious academic and personal support of Dr. Benoît Encelle. His persistence and insight are reflected in every part of this work. I would like to express the deepest appreciation to him for the attention, the commitment, and the continuous support with all the useful discussions and brainstorming sessions. I would like also to render my warmest thanks to Dr. Azze-Eddine Maredj, my mentor and first line responsible at CERIST. His advice on research, my career and many other aspects of my life have been priceless. My heartfelt appreciation goes to him for the great gifts of time, compassion and support. It gives me an immense debt of gratitude to express my sincere thanks and profound gratitude to Prof. Nadjib Badache, director of the CERIST Research Center, without whom I would probably never have done a Ph.D. at all. I am really grateful to Prof. Abdelkamel Tari for having agreed to chair the jury and review this thesis work. In addition, it is an honor to have Prof. Nadjib Badache, Dr. abdelouaheb Aloui, and Dr. Azze-Eddine Maredj as examining committee in my thesis defense. I would like to thank the OpenClassrooms staff members, especially Matthieu Nebra and Romain Kuzniak, for their cooperation and constructive exchanges. I acknowledge with gratitude their scientific and technological support, which allowed me to refine, test and validate several theoretical and practical aspects of this work. I have the chance to work in an exceptional environment at CERIST. I want to thank all of my friends and colleagues. Besides this, I would like to extend my gratitude to everyone who knowingly or unknowingly, directly or indirectly helped me in the successful completion of this work. I owe my deepest gratitude to my parents and my family for their support, trust, and endless love. Nothing could have made sense without having my wonderful wife and our kids at my side. Thank you for being here for me, against all odds. I thank the Almighty for giving me the strength and wisdom to keep going, even in moments of extreme doubt and confusion. Table of contents List of figures viii List of tablesx 1 Introduction 1 1.1 Background . 1 1.2 Rationale and problem statement . 1 1.3 Research goal, questions and objectives . 2 1.3.1 Research goal . 2 1.3.2 Research questions, objectives and methodology . 3 1.4 Research contributions . 4 1.5 Thesis outline . 6 I Background & Related Research 8 2 Engineering and reengineering educational digital documents 9 2.1 Learning in the digital age . 9 2.1.1 Learning . 9 2.1.2 Learning in the digital age . 11 2.1.3 Educational technologies . 12 2.1.4 Learning management systems . 13 2.2 Digital document and their usages in e-learning . 14 2.2.1 “Document” as a concept . 14 2.2.2 Digital documents . 14 2.2.3 Document structures . 17 2.2.4 Digital documents in education . 18 2.3 Comprehension in reading for learning . 21 2.3.1 Comprehension as a measure of reading outcome . 21 2.3.2 Digital reading and comprehension . 21 2.3.3 Factors of comprehension . 23 2.3.4 Document readability assessment . 24 2.4 Document revision . 28 2.4.1 Revision in the writing process . 28 2.4.2 The revision process . 29 2.4.3 Taxonomy of revision . 30 2.4.4 Issues in document revision . 32 2.5 Summary . 33 Table of contents v 3 Usage analytics and knowledge discovery in educational documents 34 3.1 Tracing reading usages in e-learning . 34 3.1.1 Monitoring learning . 34 3.1.2 Monitoring approaches . 35 3.1.3 Computer-mediated activity traces . 36 3.1.4 Trace-based interaction indicators . 37 3.2 Analysis of learning traces . 39 3.2.1 Knowledge discovery in digital data . 39 3.2.2 Educational data mining (EDM) and Learning analytics (LA) . 40 3.2.3 Learning analytics lifecycle . 42 3.2.4 Methods, processes, and tools in EDM/LA . 44 3.3 Learning analytics dashboards . 49 3.3.1 Information visualization and visual analytics . 49 3.3.2 Educational dashboards . 50 3.3.3 Types of dashboards . 52 3.3.4 Data used by learning dashboards . 54 3.3.5 Evaluating learning dashboards . 54 3.3.6 Limitation of the existing dashboards . 55 3.4 Summary . 57 4 Summary and discussion of the related research 58 4.1 Importance of course quality for comprehension . 58 4.2 Course revision . 59 4.3 Monitoring digital reading in e-learning . 60 4.4 Towards assistive dashboards . 61 4.5 Summary . 61 II Contributions 63 5 Usage-based document reengineering for sustaining reading and com- prehension 64 5.1 Educational document reengineering . 65 5.1.1 Document reengineering . 65 5.1.2 A conceptual framework for usage-based document reengineering 66 5.1.3 Document model . 68 5.2 Taxonomy of document reengineering actions . 69 5.2.1 Modeling reengineering . 69 5.2.2 Types of reengineering primitives . 70 5.3 Reading issues and reengineering actions related to document structures 72 5.3.1 Comprehension at the surface structure . 72 5.3.2 Comprehension at the conceptual structure . 75 5.4 Summary . 81 6 Usage-based Course Reading Analytics 82 6.1 Reading analytics approach for course revision . 82 6.1.1 Reading analytics . 82 Table of contents vi 6.1.2 Course reengineering approach based on reading analytics . 83 6.2 Modeling learners’ reading activity . 84 6.2.1 Rationale . 84 6.2.2 Reading sessions . 84 6.2.3 Constructing learners’ sessions of reading . 84 6.2.4 A dynamic and local session identification method . 88 6.3 Reading session-based indicators . 91 6.3.1 Stickiness and interest . 91 6.3.2 Rereading . 93 6.3.3 Navigation . 94 6.3.4 Reading stop & resume . 96 6.4 Indicator-based reading issue detection and revision suggestion . 98 6.4.1 Rationale . 98 6.4.2 Issue detection method . 98 6.4.3 Issues and revision suggestions related to stickiness . 99 6.4.4 Issues and revision suggestions related to rereading . 102 6.4.5 Issues and revision suggestions related to navigation . 104 6.4.6 Issues and revision suggestions related to stops & resumes . 107 6.5 Summary . 109 7 CoReaDa: The COurse READing DAshboard 110 7.1 Rational and design methodology . 110 7.1.1 Conception methodology . 110 7.1.2 Functional features . 111 7.1.3 Design methodology . 111 7.2 System architecture and technological choices . 113 7.2.1 Architecture overview . 113 7.2.2 The development stack . 114 7.3 CoReaDa Analytics (server-side) . 115 7.3.1 Application manager . 115 7.3.2 Analytics engine . 117 7.3.3 Data models . 118 7.4 CoReaDa User Interface (front-end) . 121 7.4.1 Course analysis layout . 122 7.4.2 Help and assistance . 125 7.4.3 System administration . 125 7.5 Summary . 127 8 Evaluation and validation of the proposals 128 8.1 Evaluation objectives and settings . 128 8.1.1 Study context . 128 8.1.2 Objectives . 129 8.1.3 Participants and data used .