Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems

In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient and useful guide through the contents of the educational system is obtained.Publicad

Intelligent Tutoring System Sebagai Upaya Inovatif dalam Pembelajaran untuk Pembelajaran Berbantuan Komputer

Perkembangan teknologi informasi dan komunikasi yang pesat juga telah merambah bidang pendidikan dan pengajaran. Penggunaan pembelajaran berbasis komputer dalam pembelajaran telah diteliti dan memberikan dampak positif dalam pembelajaran. Salah satu pembelaajran berbasis komputer yang saat ini masih  terus dikembangkan adalah  Intelligent Tutoring System (ITS) yang dikembangkan untuk mengatasi kelemahan pembelajaranberbasis komputer sebelumnya yang belum memperhatikan keberagaman siswa. ITS merupakan sebuah aplikasi komputer yang dibuat untuk meniru mimik manusia dalam memberikan materi pengajaran. ITS menggunakan pendekatan one-to-one.  ITS merupakan sistem yang cerdas karena memiliki komponen kecerdasan buatan

Intelligent Tutoring System sebagai Upaya Inovatif dalam Pembelajaran untuk Pembelajaran Berbantuan Komputer

Perkembangan teknologi informasi dan komunikasi yang pesat juga telah merambah bidang pendidikan dan pengajaran. Penggunaan pembelajaran berbasis komputer dalam pembelajaran telah diteliti dan memberikan dampak positif dalam pembelajaran. Salah satu pembelaajran berbasis komputer yang saat ini masih terus dikembangkan adalah Intelligent Tutoring System (ITS) yang dikembangkan untuk mengatasi kelemahan pembelajaran berbasis komputer sebelumnya yang belum memperhatikan keberagaman siswa. ITS merupakan sebuah aplikasi komputer yang dibuat untuk meniru mimik manusia dalam memberikan materi pengajaran. ITS menggunakan pendekatan one-to-one. ITS merupakan sistem yang cerdas karena memiliki komponen kecerdasan buatan

Métodos e design de sistemas de hipermídia adaptativa educacionais

Este artigo apresenta um estudo sobre métodos e design de sistemas de hipermídias adaptativos no contexto educacional. Inicialmente, uma revisão de métodos e técnicas mais utilizadas em um sistema adaptativo são descritos e discutidos. Em seguida, são apresentados alguns estudos sobre o design de hipermídias adaptativas, além de princípios de design da informação propostos por Pettersson (2012). A partir do estudo dos métodos e design de sistemas de hipermídias, são propostas linhas-guias que tem como objetivo auxiliar desenvolvedores e designers no desenvolvimento de sistemas de hipermídias adaptativos educacionais (SHAE). Por fim, são discutidas as interações entre método e design nestes sistemas

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

Modélisation des interfaces utilisateur intelligentes pour les environnements informatisés dédiés à l'apprentissage humain

Les interfaces utilisateur intelligentes (IUI) est un domaine de recherche relativement jeune et pluridisciplinaire, car il fait appel à l'ingénierie des connaissances et au domaine des interfaces personne-machine. Nous avons choisi un cadre spécifique, les environnements informatisés dédiés à l'apprentissage humain (EIAH), pour ainsi bien cibler nos recherches. En étudiant la documentation scientifique sur les IUI dans des EIAH, nos recherches ont montré qu'il y a absence de modèle pour construire des IUI efficaces. Pour remédier ce problème, nous proposons un modèle générique d'IUI qui intègre cinq composantes d'interface pour améliorer les EIAH. Ce modèle a été construit à partir de l'étude des critères d'évaluation d'interfaces et de l'étude d'IUI dans des EIAH existantes. Ce modèle a pour but de créer des IUI qui répondent mieux aux besoins des utilisateurs, qui améliorent l'interaction entre l'humain et la machine, et donc aident les personnes dans leur apprentissage. De plus, il se veut un guide afin de faciliter la conception de ces IUI. Ce modèle a été mis à l'épreuve par une évaluation heuristique par des experts afin de démontrer que notre modèle atteint bien ses objectifs. L'intégration des cinq composantes rend les IUI plus efficaces et de meilleure qualité et leur description peut facilement être réutilisable pour toute autre conception d'IUI de EIAH. Cependant, la mise à jour régulière du modèle s'avère nécessaire et une description plus formelle des composantes pourraient être proposées

Utiliza??o de M?dia M?vel Exponencialmente Ponderada para detectar e corrigir os Estilos de Aprendizagem do estudante

Incluir a Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) como ag?ncia financiadora.Na modalidade de ensino a dist?ncia, os Ambientes Virtuais de Aprendizagem (AVAs) s?o elementos fundamentais no processo de ensino e aprendizagem, atrav?s da disponibiliza??o de conte?dos e ?reas de discuss?o e comunica??o entre os atores do processo. Entretanto, tais ambientes, na sua maioria, caracterizam-se pelo fato de serem est?ticos, abordando m?todos pedag?gicos gen?ricos atrav?s dos quais estudantes com caracter?sticas e Estilos de Aprendizagem (EAs) diferentes buscam o conhecimento. Dessa maneira, ? importante que sejam levados em considera??o os EAs de cada estudante como forma de tornar a aprendizagem mais eficaz. Question?rios psicom?tricos na maioria das vezes s?o utilizados para que as caracter?sticas de aprendizagem do estudante sejam identificadas, por?m nem sempre tais question?rios apresentam resultados precisos quanto ao EAs de determinado estudante. Assim, faz-se necess?ria a utiliza??o de outras t?cnicas de detec??o, haja vista que uma identifica??o precisa ? capaz de melhorar o processo de aprendizagem por meio de escolhas de estrat?gias pedag?gicas melhores. Diante disso, surge a necessidade de utiliza??o de sistemas inteligentes que se adaptem ?s caracter?sticas de aprendizagem do estudante, utilizando como pressupostos as experi?ncias vivenciadas por ele e as an?lises estat?sticas dessas experi?ncias. Isso pode ser feito atrav?s de avalia??es dos EAs apresentados pelo estudante, em que a partir dos resultados um novo modelo de aprendizagem do estudante ? definido para que o conte?do seja disponibilizado de acordo com esse modelo. Nesse intuito a presente abordagem objetivou identificar e corrigir os EAs do estudante por meio da utiliza??o do conceito de M?dia M?vel Exponencialmente Ponderada no processo de decis?o sobre a aplica??o do refor?o de maneira a ajustar o Modelo do Estudante (ME), de modo que os resultados obtidos, ap?s a realiza??o do teste estat?stico n?o-param?trico de Mann-Whitney, mostraram-se significativamente melhores do que os resultados apresentados por Dor?a (2012), cujo trabalho foi refer?ncia para o desenvolvimento desta proposta.Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.In Distance Learning, Learning Management Systems (LMS) are extremely important elements in teaching and learning process, because they can offer content and spaces of discussion and comunication between people who are part of that process. However they are static and do not consider students? Learning Styles (LS) to show the content, they just use the same pedagogical methods for all learners. It is important to consider students? Learning Styles because this can make the learning process more efective. Most of the time people use Psychometric Instruments to detect students? preferences, but sometimes the outcomes of those methods are not precise. Because of this other techniques of detection of LS can be used to identify precisely the student?s LS and consequently to choose better pedagogical strategies than when are used manual techniques of detection of LS. For this reason intelligent systems which adapt to students? learning characteristics get importance since they use experiences and statistical analysis over these experiences to be adaptive. It can be done based on learner?s Learning Styles that are adjusted by a part of the system, then these new LS are used by another part of the system to select a pedagogical strategy which fit to student?s characteristics. Thus, this work presents an approach which aimed to identify and to correct the Learning Styles of the learner using for this the Exponentially Weighted Moving Average (EWMA) concept. This concept was used to decide if reinforcement signs have to be used to make the student?s modeling. This approach was tested and the outcomes were submitted to non parametric test Mann-Whitney which pointed they were significantly better than the results of Dor?a (2012), whose work was the base of the work presented here

A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

Metamodel for personalized adaptation of pedagogical strategies using metacognition in Intelligent Tutoring Systems

The modeling process of metacognitive functions in Intelligent Tutoring Systems (ITS) is a difficult and time-consuming task. In particular when the integration of several metacognitive components, such as self-regulation and metamemory is needed. Metacognition has been used in Artificial Intelligence (AI) to improve the performance of complex systems such as ITS. However the design ITS with metacognitive capabilities is a complex task due to the number and complexity of processes involved. The modeling process of ITS is in itself a difficult task and often requires experienced designers and programmers, even when using authoring tools. In particular the design of the pedagogical strategies for an ITS is complex and requires the interaction of a number of variables that define it as a dynamic process. This doctoral thesis presents a metamodel for the personalized adaptation of pedagogical strategies integrating metamemory and self-regulation in ITS. The metamodel called MPPSM (Metamodel of Personalized adaptation of Pedagogical Strategies using Metacognition in intelligent tutoring systems) was synthetized from the analysis of 40 metacognitive models and 45 ITS models that exist in the literature. MPPSMhas a conceptual architecture with four levels of modeling according to the standard Meta- Object Facility (MOF) of Model-Driven Architecture (MDA) methodology. MPPSM enables designers to have modeling tools in early stage of software development process to produce more robust ITS that are able to self-regulate their own reasoning and learning processes. In this sense, a concrete syntax composed of a graphic notation called M++ was defined in order to make the MPPSM metamodel more usable. M++ is a Domain-Specific Visual Language (DSVL) for modeling metacognition in ITS. M++ has approximately 20 tools for modeling metacognitive systems with introspective monitoring and meta-level control. MPPSM allows the generation of metacognitive models using M++ in a visual editor named MetaThink. In MPPSM-based models metacognitive components required for monitoring and executive control of the reasoning processes take place in each module of an ITS can be specified. MPPSM-based models represent the cycle of reasoning of an ITS about: (i) failures generated in its own reasoning tasks (e.g. self-regulation); and (ii) anomalies in events that occur in its Long-Term Memory (LTM) (e.g. metamemory). A prototype of ITS called FUNPRO was developed for the validation of the performance of metacognitive mechanism of MPPSM in the process of the personalization of pedagogical strategies regarding to the preferences and profiles of real students. FUNPRO uses self-regulation to monitor and control the processes of reasoning at object-level and metamemory for the adaptation to changes in the constraints of information retrieval tasks from LTM. The major contributions of this work are: (i) the MOF-based metamodel for the personalization of pedagogical strategies using computational metacognition in ITS; (ii) the M++ DSVL for modeling metacognition in ITS; and (iii) the ITS prototype called FUNPRO (FUNdamentos de PROgramación) that aims to provide personalized instruction in the subject of Introduction to Programming. The results given in the experimental tests demonstrate: (i) metacognitive models generated are consistent with the MPPSM metamodel; (ii) positive perceptions of users with respect to the proposed DSVL and it provide preliminary information concerning the quality of the concrete syntax of M++; (iii) in FUNPRO, multi-level pedagogical model enhanced with metacognition allows the dynamic adaptation of the pedagogical strategy according to the profile of each student.Doctorad