京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24728号 / 情博第816号 / 新制||情||137(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 河原 達也, 教授 熊田 孝恒, 教授 黒橋 禎夫 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Les études empiriques sur les technologies mobiles dans le cadre de l'éducation sont rares. Selon la revue de la littérature, différentes études théoriques critiquent les modèles actuels d'acceptation et d'adoption de la technologie mais peu d'études ont été menées sur le terrain. Il existe également peu de données empiriques sur la stratégie de l’université en matière d’adoption et de mise en œuvre de la technologie mobile. Nos propres recherches confirment le manque de telles études empiriques, en particulier en ce qui concerne les réponses des utilisateurs à la technologie mobile et leurs stratégies d'adaptation. De plus, la revue de la littérature suggère un manque de consensus théorique sur l'adaptation de la technologie mobile dans l'éducation. La théorie montre que l'éducation a besoin d'une vision holistique de l'adoption de la technologie mobile et de la recherche de ses différents aspects et composants. L’objectif de notre recherche est de comprendre la stratégie de l'université pour adopter et mettre en œuvre la technologie mobile. Cette recherche se structure autour de deux questions principales : Pourquoi la technologie mobile est-elle utilisée dans l’enseignement supérieur ? (au niveau stratégique) Comment la technologie mobile est-elle utilisée dans l’enseignement supérieur ? Cette thèse est une recherche exploratoire. Afin de laisser la théorie émerger des résultats empiriques, cette thèse est inspirée de la théorie enracinée. Au total, deux études de cas ont été menées. Deux écoles d'ingénieur françaises ont été choisies comme nos terrains de recherche. Première étude de cas réalisée par observation directe pendent 6 mois dans une école d'ingénieur A (1155 heures d’observations) comprenant 193 étudiants et 88 enseignants. Deuxième étude de cas réalisée par observation directe pendant 4 mois dans une école d'ingénieur B (704 heures d’observations) comprenant 115 étudiants et 29 enseignants. De plus, 15 entretiens semi-directifs ont été réalisés avec des professeurs en charge du projet de la technologie mobile, le directeur de l’université, le directeur du département et le personnel informatique et administratif. Toutes les données ont été codées et analysées. Au cours de cette recherche, nous avons constamment comparé nos résultats, les données codées, les incidents émergents et les concepts émergents pour générer des catégories et les comparer avec les résultats des terrains de recherche. Les apports principaux peuvent être classés en quatre catégories : Utilisation de la technologie mobile (selon les réponses des étudiants et des enseignants). Une analyse des perceptions du participant sur la technologie mobile adoptée. Une analyse des activités d'adaptation des participants (relatif à TI, aux tâches et l’individu) et trois phases du processus d'adaptation (au niveau individuel, organisationnel et du groupe). Une étude de la stratégie principale de l’université vers l’adoption de la technologie mobile et des moyens engagés pour soutenir les changements en vue de l'appropriation de la technologie mobile. En conclusion nous préconisons des implications managériales et théoriques et différents axes de recherche qui pourraient être développés ultérieurement en vue d’affiner le travail réalisé pour cette thèse et aussi afin de tester et généraliser les résultats aujourd'hui obtenus. / Empirical studies of mobile technology in education are scarce. According to the literature review, different theoretical studies criticizing the current models of technology acceptance and adoption are seeking for changes, but not many research works have been conducted in the field. There are few empirical data about the university’s strategy in adoption and implementation of mobile technology as well.This research confirms the lack of such empirical studies, especially regarding user’s responses to mobile technology and their adaptation strategies in education. Hence, more rigorous research is needed to understand the perceptions and adaptation process of university’ participants. Furthermore, the literature review suggests a lack of theoretical consensus on adaptation of mobile technology in education.Theory shows that the education needs a holistic view of mobile technology adoption and investigation of its different aspects and components. Few research works were conducted in investigating a whole organizational implementation.This dissertation aims at understanding the strategy of university for adopting and implementing the mobile technology.This research is articulated around two main research questions:• Why is mobile technology used in higher education? (at the strategic level)• How is mobile technology used in higher education?This study is an exploratory research in order to allow the theory to emerge from the empirical results and this research is inspired from the Grounded Theory.In total, two case studies were conducted. Two French engineering universities were selected as our fields of study:• First case study is carried out during a 6-month direct observation at engineering university A (equal to 1155 hours) including 193 students and 88 faculty members.• Second case study is carried out during a 4-month direct observation at engineering university B (equal to 704 hours) including 115 students and 29 faculty members.In addition, 15 semi-directive interviews were conducted with professors in charge of mobile technology project, the university’ director, head of the department as well as IT and administrative staff. All data were coded and analyzed.During this research, we constantly compared our findings and the coded data to the emerging incidents and to the emerging concepts with the purpose of generating categories and comparing with the findings acquired from the fields of research.The main contributions can be classified into four categories:• Utilization of mobile technology (according to students, faculty members use),• An analysis of the participant’s perceptions of the adopted mobile technology• An analysis of participant’s coping activities (IT related, Task related and self-related) and three phases of adaptation process (individual, organizational, and group level)• A study of the main university’s strategy towards the adoption of mobile technology and how far it supports changes towards mobile technology appropriation.The conclusion brings managerial implications, theoretical implications and some recommendations for further studies in order to deepen the research and to answer several hypotheses issued from our results.

Human interactions with Information Technology tools are reproducing organisational cultural patterns in a process similar to the evolution of human tools and language. A multidisciplinary research in tool-mediated activity, culture, language and cognition will examine new concepts that can be important for the design of organisationally fit Information Technology interface tools. By using qualitative and quantitative analysis together with the fields of anthropology, philosophy, cognitive sciences and human computer interaction this thesis shows that cultural fitness is an important variable that can determine in a substantial degree the rejection or adoption of a tool in organisational environment. Qualitative and quantitative data collected from organisational simulations at the Faculty of Information Sciences and Engineering of the University of Canberra during the period 1995-1997 has been used and analysed.

Activity recognition research in computer vision and pervasive computing has made a remarkable trajectory from distinguishing full-body motion patterns to recognizing complex activities. Manipulation activities as occurring in food preparation are particularly challenging to recognize, as they involve many different objects, non-unique task orders and are subject to personal idiosyncrasies. Video data and data from embedded accelerometers provide complementary information, which motivates an investigation of effective methods for fusing these sensor modalities. This thesis proposes a method for multi-modal recognition of manipulation activities that combines accelerometer data and video at multiple stages of the recognition pipeline. A method for accelerometer tracking is introduced that provides for each accelerometer-equipped object a location estimate in the camera view by identifying a point trajectory that matches well the accelerometer data. It is argued that associating accelerometer data with locations in the video provides a key link for modelling interactions between accelerometer-equipped objects and other visual entities in the scene. Estimates of accelerometer locations and their visual displacements are used to extract two new types of features: (i) Reference Tracklet Statistics characterizes statistical properties of an accelerometer's visual trajectory, and (ii) RETLETS, a feature representation that encodes relative motion, uses an accelerometer's visual trajectory as a reference frame for dense tracklets. In comparison to a traditional sensor fusion approach where features are extracted from each sensor-type independently and concatenated for classification, it is shown that combining RETLETS and Reference Tracklet Statistics with those sensor-specific features performs considerably better. Specifically addressing scenarios in which a recognition system would be primarily used by a single person (e.g., cognitive situational support), this thesis investigates three methods for adapting activity models to a target user based on user-specific training data. Via randomized control trials it is shown that these methods indeed learn user idiosyncrasies. All proposed methods are evaluated on two new challenging datasets of food preparation activities that have been made publicly available. Both datasets feature a novel combination of video and accelerometers attached to objects. The Accelerometer Localization dataset is the first publicly available dataset that enables quantitative evaluation of accelerometer tracking algorithms. The 50 Salads dataset contains 50 sequences of people preparing mixed salads with detailed activity annotations.