Cognition and multimedia design for complex learning

Van Merriënboer, J. J. G. (1999). Cognition and Multimedia Design for Complex Learning. Inaugural address, Open University of the Netherlands, The Netherlands

Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools

Cette thèse regroupe des articles d'apprentissage automatique et s'articule autour de deux thématiques complémentaires. D'une part, les trois premiers articles examinent l'application des réseaux de neurones artificiels aux problèmes du traitement automatique du langage naturel (TALN). Le premier article introduit une structure codificatrice-décodificatrice avec des réseaux de neurones récurrents pour traduire des segments de phrases de longueur variable. Le deuxième article analyse la performance de ces modèles de `traduction neuronale automatique' de manière qualitative et quantitative, tout en soulignant les difficultés posées par les phrases longues et les mots rares. Le troisième article s'adresse au traitement des mots rares et hors du vocabulaire commun en combinant des algorithmes de compression par dictionnaire et des réseaux de neurones récurrents. D'autre part, la deuxième partie de cette thèse fait abstraction de modèles particuliers de réseaux de neurones afin d'aborder l'infrastructure logicielle nécessaire à leur définition et entraînement. Les infrastructures modernes d'apprentissage profond doivent avoir la capacité d'exécuter efficacement des programmes d'algèbre linéaire et par tableaux, tout en étant capable de différentiation automatique (DA) pour calculer des dérivées multiples. Le premier article aborde les défis généraux posés par la conciliation de ces deux objectifs et propose la solution d'une représentation intermédiaire fondée sur les graphes. Le deuxième article attaque le même problème d'une manière différente: en implémentant un code source par bande dans un langage de programmation dynamique par tableau (Python et NumPy).This thesis consists of a series of articles that contribute to the field of machine learning. In particular, it covers two distinct and loosely related fields. The first three articles consider the use of neural network models for problems in natural language processing (NLP). The first article introduces the use of an encoder-decoder structure involving recurrent neural networks (RNNs) to translate from and to variable length phrases and sentences. The second article contains a quantitative and qualitative analysis of the performance of these `neural machine translation' models, laying bare the difficulties posed by long sentences and rare words. The third article deals with handling rare and out-of-vocabulary words in neural network models by using dictionary coder compression algorithms and multi-scale RNN models. The second half of this thesis does not deal with specific neural network models, but with the software tools and frameworks that can be used to define and train them. Modern deep learning frameworks need to be able to efficiently execute programs involving linear algebra and array programming, while also being able to employ automatic differentiation (AD) in order to calculate a variety of derivatives. The first article provides an overview of the difficulties posed in reconciling these two objectives, and introduces a graph-based intermediate representation that aims to tackle these difficulties. The second article considers a different approach to the same problem, implementing a tape-based source-code transformation approach to AD on a dynamically typed array programming language (Python and NumPy)

ID for Competency−based Learning: New Directions for Design, Delivery and Diagnosis

Currently, there is a clear trend towards competency−based learning. But Instructional Design models provide yet little guidance for the development of such competency−based instructional systems. It is argued that rich, realistic learning tasks are always at the heart of competency−based learning. From this starting point, nine directions for a new paradigm of Instructional Design are presented: Three directions pertain to the design of learning tasks; three directions pertain to the delivery of those tasks and learning resources in multimedia learning environments, and three directions pertain to the diagnosis of learners' progress.Currently, there is a clear trend towards competency−based learning. But Instructional Design models provide yet little guidance for the development of such competency−based instructional systems. It is argued that rich, realistic learning tasks are always at the heart of competency−based learning. From this starting point, nine directions for a new paradigm of Instructional Design are presented: Three directions pertain to the design of learning tasks; three directions pertain to the delivery of those tasks and learning resources in multimedia learning environments, and three directions pertain to the diagnosis of learners' progress

ID for Competency−based Learning: New Directions for Design, Delivery and Diagnosis

Currently, there is a clear trend towards competency−based learning. But Instructional Design models provide yet little guidance for the development of such competency−based instructional systems. It is argued that rich, realistic learning tasks are always at the heart of competency−based learning. From this starting point, nine directions for a new paradigm of Instructional Design are presented: Three directions pertain to the design of learning tasks; three directions pertain to the delivery of those tasks and learning resources in multimedia learning environments, and three directions pertain to the diagnosis of learners' progress.Currently, there is a clear trend towards competency−based learning. But Instructional Design models provide yet little guidance for the development of such competency−based instructional systems. It is argued that rich, realistic learning tasks are always at the heart of competency−based learning. From this starting point, nine directions for a new paradigm of Instructional Design are presented: Three directions pertain to the design of learning tasks; three directions pertain to the delivery of those tasks and learning resources in multimedia learning environments, and three directions pertain to the diagnosis of learners' progress

Strategies for Programming Instruction in High School:Program Completion vs. Program Generation

In an introductory programming course, the differential effects on learning outcomes were studied for an experimental instructional strategy that emphasized the modification and extension of existing programs (completion strategy) and a traditional strategy that emphasized the design and coding of new programs (generation strategy). Two matched groups of twenty-eight and twenty-nine high school students from grades ten through twelve volunteered for participation in a ten-lesson programming course using a small subset of the structured programming language COMAL-80. After the course, the completion group was superior to the generation group in measures concerning the construction of programs; furthermore, it was characterized by a lower mortality. The data indicated that the completion strategy facilitated the use of templates; however, this does not necessarily seem to imply that the students actually understood the working of those templates, because no differences occurred in the ability to interpret programs. In the conclusion, the completion strategy is considered to be a good alternative to more traditional strategies and recommendations are made for further improvements