The first part of this study describes the synthesis of a research framework (known as the Transformation Framework) via the analysis of existing literature on technology-related transformation in learning and teaching. The Framework identified five Foundations that were desirable for any implementation of technology in an educational setting and also described three broad types of transformation that might be expected to occur (Institutional, Material and Behavioural). The remainder of the thesis contains a description of the application of the Framework to three science courses in the College of Science and Engineering at a large Scottish university at a point in time when they were attempting to initiate some transformation in learning and teaching, at least in part through the introduction of new technologies. The Framework was used to construct a series of specific interview questions that were designed to illuminate each possible area of transformation. Interviews were then conducted with the Undergraduate Deans who were responsible for the overall initiative of which these courses formed a part and the organisers of each of the three courses (Courses A, B and C). The interview questions were then used to construct an online survey that was used to poll the lecturers and teaching assistants involved in the delivery of each course. Finally, anonymised course marks were obtained for the three courses covering the years before, during and after the innovations were introduced. Using the Framework as a reference, the data sources were then analysed, primarily using NVivo (qualitative data) and SPSS (quantitative data), in order to identify where there may have been transformation perceived or observed, and the evidence supporting the existence of any such transformation was evaluated. Any identified transformations were then analysed further to ascertain any specific contribution that technology may have had to such change. The results provided broad support for the notion that the transformations that may occur are highly context-dependent, and are often influenced by the Foundations that are in place at the time. Course A could be described as “innovation-ready” and as such there was evidence to suggest that the technologies used had several Institutional, Material and Behavioural transformative effects. Course B was more cautious and perhaps less prepared, and yet some Institutional, Material and Behavioural transformations were observed, largely in those areas that were well attended at the Foundation stage. The Course C implementation was done at short notice, and hence with little preparation and as such was very low-key and only limited Material and Behavioural transformations were evident as a result. The research as described above highlights the fact that transformation is far more likely to occur if the proper Foundations have been put in place first, and the technology forms part of an implementation that is well thought-out by the organisers, well supported by the powers-that-be and well accepted by all those who will engage with it. The Framework itself has proved to be a useful and robust guide for this kind of study and it should have value in many different contexts in the future. Applications include not only the evaluation of existing implementations of technology in the classroom but also the planning and preparation of such implementations, informing both the design of a particular course and the choice of technology to achieve specific results.

The goal of learning transfer is to apply knowledge gained from one problem to a separate related problem. Transformation learning is a proposed approach to computational learning transfer that focuses on modeling high-level transformations that are well suited for transfer. By using a high-level representation of transferable data, transformation learning facilitates both shallow transfer (intra-domain) and deep transfer (inter-domain) scenarios. Transformations can be discovered in data using manifold learning to order data instances according to the transformations they represent. For high-dimensional data representable with coordinate systems, such as images and sounds, data instances can be decomposed into small sub-instances based on coordinates. Coordinate-based transformation models trained using these sub-instances can effectively approximate transformations from very small amounts of input data compared to the naive transformation modeling approach. In addition, these models are well suited for deep transfer scenarios.

L’ingénierie dirigée par les modèles (IDM) est un paradigme d’ingénierie du logiciel bien établi, qui préconise l’utilisation de modèles comme artéfacts de premier ordre dans les activités de développement et de maintenance du logiciel. La manipulation de plusieurs modèles durant le cycle de vie du logiciel motive l’usage de transformations de modèles (TM) afin d’automatiser les opérations de génération et de mise à jour des modèles lorsque cela est possible. L’écriture de transformations de modèles demeure cependant une tâche ardue, qui requiert à la fois beaucoup de connaissances et d’efforts, remettant ainsi en question les avantages apportés par l’IDM. Afin de faire face à cette problématique, de nombreux travaux de recherche se sont intéressés à l’automatisation des TM. L’apprentissage de transformations de modèles par l’exemple (TMPE) constitue, à cet égard, une approche prometteuse. La TMPE a pour objectif d’apprendre des programmes de transformation de modèles à partir d’un ensemble de paires de modèles sources et cibles fournis en guise d’exemples. Dans ce travail, nous proposons un processus d’apprentissage de transformations de modèles par l’exemple. Ce dernier vise à apprendre des transformations de modèles complexes en s’attaquant à trois exigences constatées, à savoir, l’exploration du contexte dans le modèle source, la vérification de valeurs d’attributs sources et la dérivation d’attributs cibles complexes. Nous validons notre approche de manière expérimentale sur 7 cas de transformations de modèles. Trois des sept transformations apprises permettent d’obtenir des modèles cibles parfaits. De plus, une précision et un rappel supérieurs à 90% sont enregistrés au niveau des modèles cibles obtenus par les quatre transformations restantes. / Model-driven engineering (MDE) is a well-established software engineering paradigm that promotes models as main artifacts in software development and maintenance activities. As several models may be manipulated during the software life-cycle, model transformations (MT) ensure their coherence by automating model generation and update tasks when possible. However, writing model transformations remains a difficult task that requires much knowledge and effort that detract from the benefits brought by the MDE paradigm. To address this issue, much research effort has been directed toward MT automation. Model Transformation by Example (MTBE) is, in this regard, a promising approach. MTBE aims to learn transformation programs starting from a set of source and target model pairs supplied as examples. In this work, we propose a process to learn model transformations from examples. Our process aims to learn complex MT by tackling three observed requirements, namely, context exploration of the source model, source attribute value testing, and complex target attribute derivation. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas, precision and recall higher than 90% are recorded for the four remaining ones.