Thèse de F. Lespiau : “Logique sans peine ?”

Lundi 4 décembre prochain je participerai, à l’université de Toulouse-Jean-Jaurès, au jury de la thèse de psychologie cognitive de Florence Lespiau, dont le titre est “Comment nous sommes plus performants et motivés pour raisonner logiquement à propos des connaissances primaires.”
Le jury est composé des autres membres suivants :

Lire le résumé de la thèse ci-dessous en cliquant sur le lien. Et la présentation via le laboratoire.

Voici le résumé de la thèse :
L’apprentissage donne souvent l’impression d’être un processus long et difficile, notamment quand il fait penser à l’école et à la difficulté que tout le monde a déjà ressentie pour maintenir sa motivation pour telle ou telle matière.
Pourtant, il y a des choses que l’on apprend sans enseignement. Par exemple, apprendre à parler sa langue maternelle se fait naturellement sans effort conscient. Les connaissances primaires et secondaires sont une façon de distinguer ce qui est facile ou difficile à apprendre. Les connaissances primaires sont celles pour lesquelles nos mécanismes cognitifs auraient évolué, permettant une acquisition sans effort, intuitive et rapide alors que les connaissances secondaires sont apparues récemment : ce sont celles pour lesquelles nous n’aurions pas eu le temps d’évoluer et dont l’acquisition serait longue et coûteuse. Les écoles se focalisent essentiellement sur ce deuxième type de connaissances. Leur défi est de permettre ces apprentissages longs et coûteux, et, pour cela, de maintenir la motivation des apprenants. Une piste de recherche s’appuie sur le fait que les connaissances secondaires sont construites sur la base des connaissances primaires. En effet, personne n’est capable d’enseigner « initialement » une langue maternelle alors que l’apprentissage des langues étrangères s’appuie sur cette première langue.
Le présent travail explore le caractère motivant et peu coûteux des connaissances primaires pour faciliter l’apprentissage de la logique en tant que connaissance secondaire. En modifiant la présentation de problèmes logiques avec des habillages liés aux connaissances primaires (e.g., nourriture et caractéristiques d’animaux) ou secondaires (e.g., règles de grammaire, mathématiques), huit premières expériences ont permis de mettre en avant les effets positifs des connaissances primaires que le contenu soit familier ou non. Les résultats montrent que les connaissances primaires favorisent la performance, l’investissement émotionnel, la confiance dans les réponses et diminuent la charge cognitive perçue.
Quant aux connaissances secondaires, elles semblent miner la motivation des participants et générer une sensation de conflit parasite. De plus, présenter des problèmes avec un habillage de connaissances primaires en premier permettrait de réduire les effets délétères des connaissances secondaires présentées ensuite et aurait un impact positif global. Trois autres expériences ont alors mis ces résultats à l’épreuve de tâches d’apprentissage afin de proposer une approche qui favorise l’engagement des apprenants et leur apprentissage. Ces découvertes tendent à montrer que les recherches sur l’apprentissage bénéficieraient à prendre en considération les connaissances primaires plutôt que de les négliger car elles sont « déjà apprises ».

Gutu and Paraschiv PhD Thesis Committees

On Thursday 28th, Nov., I’ll be attending the PhD thesis committee of two students from University ‘Politehnica’ of Bucharest, Gabriel Gutu and Ionut Paraschiv, supervised by Stefan Trausan-Matu.
Gabriel Gutu‘s thesis is entitled “Discourse Analysis based on Semantic Modelling and Textual Complexity” and aims at extending some ReaderBench‘s functionalities in the domain of CSCL discussion analysis.
Ionut Paraschiv’s thesis (Semantic Meta-Annotation and Comprehension Modeling) also adds features to ReaderBench, in comprehension modeling and scientometrics.
For more information, read below their summaries.

Gabriel Gutu thesis’ summary

The exponential growth of digital documents, together with the necessity for analysis and extraction of valuable information within them, bring routine work for people. The opportunity for development of automated discourse analysis services and techniques leads to automation of laborious operations. In the long run, the transferring of tiresome operations into computerized systems would allow people to focus on “high level” assignments that lead to interesting ideas and provide the means to extract thoughts and understandings that are currently hardly interpreted by computers.
Discourse analysis refers to the extraction of relevant information from documents by using techniques of analysis known in scientific literature as Natural Language Processing. The services presented in this thesis make use of recent advancements in the field by integrating semantic models and textual complexity factors. Semantic models allow the mapping of documents into mathematical representations that provide comparison and scoring for units of texts, be them either simple words, sentences, paragraphs or even entire documents. Of the semantic models, the thesis relies on Latent Semantic Analysis, Latent Dirichlet Allocation and the more recent Word2vec. The WordNet ontology is the lexicon used as alternative to semantic models. Compared to semantic models, a lexicon expresses “more natural” relations between units of texts because of relying on a dictionary and using relations between words that are created in collaboration with linguists.
The experiments were performed by extending ReaderBench, a multi-lingual Natural Language Processing open-source framework. Two directions were followed: 1) analysis of Computer Supported Collaborative Learning (CSCL) chat conversations; 2) automation of processes of discourse analysis through mechanisms adaptable to various scenarios relying on textual content. The studies performed on CSCL conversations targeted the idea of developing an automated mechanism of detection of implicit links, facility that is missing in chat platforms. By integrating such a mechanism, the outlined relations between utterances may ease processes like detection of topics, voices or lexical chains. The researches performed on documents included automated classification of documents, assessment of documents’ quality or automated scoring of students’ assignments in a Massive Open Online Course platform. The mechanisms were validated on real world data extracted. Services were exposed through an Application Programming Interface.
The author of this thesis beliefs that the presented experiments could provide ideas for future studies and could ease the involved work by allowing researchers to focus on their topics while relying on the validated mechanisms by using the implemented services made available through the open-source ReaderBench framework.

Ionut Paraschiv thesis’ summary

Each domain, alongside its knowledge base, changes over time and each period is centered on specific topics that emerge from different ongoing research projects. A researcher’s daily activities usually involve the study of new papers and using the information for building solutions and observing how the domain evolves. Since the retrieval of documents from the Internet can lead to large data flows, it is important to consider other approaches for a more comprehensive analysis of the domain. In this context, the Semantic Meta Annotations focus on building a scalable paper annotation system that automatically retrieves papers on a given topic and tags them, to make the exploration phase of the research literature substantially easier.
Evolution is based on leveraging existing knowledge, researches and tools to test other ideas. A researcher needs to read many textual materials, which are many times cluttered with irrelevant information. Thus, the focus of our research is shifted towards understanding the way in which humans comprehend texts. Reading is a complex cognitive process which has been the subject of many studies throughout the years. It is one of the oldest ways for learners to acquire new information and to consolidate existing knowledge, representing a key evolutionary element. Each textual material contains facts and topics that activate existing concepts from the reader’s prior knowledge (memory). The Comprehension Model describes an automated method that analyzes the way in which readers potentially assimilate and conceptualize new text information, which is a novel alternative for indexing and meta-annotating textual corpora. Creating such a method is a challenge, as it requires using a computational knowledge base, parsing unstructured textual materials and linking concepts using various heuristics and semantic similarity measures.
Our research focuses on the semantic analysis of unstructured textual materials by using Natural Language Processing techniques and models such as Latent Semantic Analysis, Latent Dirichlet Allocation, Word2Vec or semantic distances within lexicalized ontologies, i.e., WordNet. Within the experiments focused on semantic meta annotations, these distances are combined with other metrics such as co-citation analysis or co-authorship, thus creating the basis of several interactive and exploratory visual graphs that offer a better domain overview within a scalable infrastructure. In the second experiment, our focus is shifted towards describing an automatic comprehension modelling technique that analyzes using computational representations and algorithms the reading process. Our goal was to create a set of methods and tools to help researchers in their daily work to easily retrieve and understand textual materials.