Behavioural cloning of teachers for automatic homework selection

© Springer Nature Switzerland AG 2019. We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.Cambridge Assessmen

Agoraphobia and the modern learner.

Read/write social technologies enable rich pedagogies that centre on sharing and constructing content but have two notable weaknesses. Firstly, beyond the safe, nurturing environment of closed groups, students participating in more or less public network- or set-oriented communities may be insecure in their knowledge and skills, leading to resistance to disclosure. Secondly, it is hard to know who and what to trust in an open environment where others may be equally unskilled or, sometimes, malevolent. We present partial solutions to these problems through the use of collective intelligence, discretionary disclosure controls and mindful design

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Sistemas recomendadores aplicados en Educación

En este trabajo final integrador se analizaron diferentes técnicas de recomendación y se estudió su aplicabilidad en el ámbito educativo. Así también se presenta un resumen de las métricas usualmente utilizadas para medir la performance de éstos sistemas y cuáles son las variantes o nuevas métricas a tener en cuenta cuando se aplican éstos sistemas en educación. En el trabajo experimental se utilizaron diferentes conjuntos de datos de prueba abordados en la literatura de los SRE y se compararon los resultados obtenidos con distintos algoritmos de recomendación basados en la técnica de Filtrado Colaborativo (FC).Facultad de Informátic

Providing Service-based Personalization in an Adaptive Hypermedia System

Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large.In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content.To empirically prove the viability of our approach, we developed PERSEUS - server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Estratégia híbrida de recomendações num gestor de conteúdos ampliado

O objetivo principal desta dissertação é propor um modelo de recomendação de conteúdos de aprendizagem num ambiente formal, típico do Ensino Superior, sobre um gestor de conteúdos ampliado, onde os alunos avaliam e publicam conteúdos. As principais contribuições deste trabalho são: 1. A adição de conteúdos pelos alunos é por eles bem aceite; 2. Modelo de recomendações híbrido, em cascata, com três filtragens baseadas em regras de precedência, duração de estudo e avaliação das atividades de aprendizagem; 3. Formulação de similaridade entre alunos, que valoriza os melhores alunos e o passado recente de conteúdos selecionados por cada aluno; 4. Implementação, experimentação e avaliação de um protótipo baseado no modelo proposto. A avaliação mostrou que o desenvolvimento deste tipo de sistemas permite aos alunos terem experiências únicas de aprendizagem com sequenciamentos de conteúdos adequados aos seus perfis. Destacamos a contribuição para esta investigação da revisão de conceitos e literatura relacionada; ABSTRACT:The main objective of this thesis is to propose a model for personalized recommendation of learning activities, directed to learners, in a formal learning context. We suggest that learners can publish some useful contents and that they should rate them. The main contributions of this work are: 1. The addition of new content was well accepted, by learners; 2. Hybrid recommendations model in cascade, with three filtering techniques, based on precedence rules, duration of study and classification of learning activities; 3. New formulation of similarity between students, which gives value to the best students and recent past of content selected by each student. 4. A prototype has been implemented and real experimentation was carried out during two months. It showed that such systems help learners to diversify their learning paths and experiences, increase useful collaboration and support making decisions. We also highlight the contribution of the revision of related work

Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

Vuorikari, R. (2009). Tags and self-organisation: a metadata ecology for learning resources in a multilingual context. Doctoral thesis. November, 13, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC.This thesis studies social tagging of learning resources in a multilingual context. Social tagging and its end products, tags, are regarded as part of the learning resources metadata ecology. The term “metadata ecology” is used to mean the interrelation of conventional metadata and social tags, and their interaction with the environment, which can be understood as the repository in the large sense (resources, metadata, interfaces and underlying technology) and its community of users. The main hypothesis is that the self-organisation aspect of a social tagging system on a learning resource portal helps users discover learning resources more efficiently. Moreover, user-generated tags make the system, which operates in a multilingual context, more robust and flexible. Social tags offer an interesting aspect to study learning resources, its metadata and how users interact with them in a multilingual context. Tags, as opposed to conventional metadata description such as Learning Object Metadata (LOM), are free, non-hierarchical keywords that end-users associate with a digital artefact, e.g. a learning resource. Tags are formed by a triple of (user,item,tag). Tags and the resulting networks, folksonomies, are commonly modelled as tri- partite hypergraphs. This ternary relational structure gives rise to a number of novel relations to better understand, capture and model contextual information. This thesis first provides two exploratory studies to better understand how users tag learning resources in a multilingual context and to find evidence on the “cross-boundary use” of learning resources. The term cross-boundary use means that the user and the resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. The second part introduces a trilogy of studies focusing on self-organisation, flexibility and robustness of a social tagging system using empirical, behavioural data captured from log-files and user’s attention metadata trails on a number of learning resource portals and platforms in a multilingual context