Collaborative reflection : supporting one practitioner's development of online learning communities /

This is a study of one participant's reflective practice as she worked to develop online communities in a face-to-face science course. Her process of reflective practice was examined in order to address factors that influenced her learning path, and the benefits and challenges of collaborative action research. These research goals were pursued using a collaborative action research methodology, initially chosen for its close match with Schon's (1983) model of reflective practice. The participant's learning fit vnth Mezirow's (1991) model of transformative learning. She began with beliefs that matched her goals, and she demonstrated significant learning in three areas. First, she demonstrated instrumental learning around the constraints of workload and time, and achieving online learning community indicators. Second, she demonstrated communicative learning that helped her to see her own needs for feedback and communication more clearly, and how other process partners had been a support to her. Third, her emancipatory learning saw her revisiting and questioning her goals. It was through the reflective conversation during the planned meetings and the researcher's reframing and interrogation of that reflection that the participant was able to clarify and extend her thinking, and in so doing, critically reflect on her practice as she worked to develop online learning communities. In this way, the collaborative action research methodology was an embodiment of co-constructivism through collaborative reflective practice. Schon's (1983) model of reflective practice positions a lone practitioners moving through cycles ofplan-act-observe-reflect. The results fi"om this study suggest that collaboration is an important piece of the reflective practice model.

Using multimedia and peer assessment to promote collaborative e-learning

Collaborative e-learning is increasingly appealing as a pedagogical approach that can positively affect student learning. We propose a didactical model that integrates multimedia with collaborative tools and peer assessment to foster collaborative e-learning. In this paper, we explain it and present the results of its application to the “International Seminars on Materials Science” online course. The proposed didactical model consists of five educational activities. In the first three, students review the multimedia resources proposed by the teacher in collaboration with their classmates. Then, in the last two activities, they create their own multimedia resources and assess those created by their classmates. These activities foster communication and collaboration among students and their ability to use and create multimedia resources. Our purpose is to encourage the creativity, motivation, and dynamism of the learning process for both teachers and students.

How online applications can help to achieve integral learning

The development of transversal competencies provides an integral education. However, its practical implementation among different subjects is not a trivial task. There are several issues that should be previously solved in an optimal way to take advantage of the synergy among subjects. Main issues are: i) the need for a common space for the documents management, ii) the availability of the document everywhere and anytime, and iii) the possibility to collaborate in the documents edition tasks. It was implemented a virtual portfolio for the students which allows the assessment of all the subjects in a global way. To this goal we used the Google apps due to its free access, availability and suitability for the collaborative editing tasks.

Designing Massive Open Online Learning Processes: The importance of the social element

This is a pre-print for personal use only. Please refer to the Springer website for the official, published version http://www.springer.com/978-3-662-52923-2

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Collaborative technologies, higher order thinking and self-sufficient learning: A case study of adult learners

The inclusion of online elements in learning environments is becoming commonplace in Post Compulsory Education. A variety of research into the value of such elements is available, and this study aims to add further evidence by looking specifically at the use of collaborative technologies such as online discussion forums and wikis to encourage higher order thinking and self-sufficient learning. In particular, the research examines existing pedagogical models including Salmon’s five-stage model, along with other relevant literature. A case study of adult learners in community-based learning centres forms the basis of the research, and as a result of the findings, an arrow model is suggested as a framework for online collaboration that emphasises the learner, mentions pre-course preparation and then includes three main phases of activity: post, interact and critique. This builds on Salmon’s five-stage model and has the benefit of being flexible and responsive, as well as allowing for further development beyond the model, particularly in a blended learning environment.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Nostalgia Studio. A game engine for creating collaborative multiplayer online graphic adventure games

No decorrer do projeto SELEAG foi desenvolvido um jogo de aventura gráfica educativo com o propósito de ensinar história, cultura e relações sociais aos alunos. Este jogo foi avaliado em contexto de sala de aula em diversos países, obtendo resultados positivos. No entanto, por motivos técnicos, alguns dos objetivos propostos pelo projeto não puderam ser devidamente explorados, como permitir que o jogo fosse extensível por outros educadores ou suportar a colaboração online entre os jogadores. Nomeadamente, as ferramentas utilizadas para desenvolver o jogo eram demasiado complicadas para serem utilizadas fora da equipa de desenvolvimento, o que limitou a extensibilidade do projeto, e tornou impossível que educadores sem conhecimentos de programação fossem também capazes de traduzir os seus conteúdos educativos para este formato. Além disso, apesar do jogo possuir algumas funcionalidades de colaboração online, toda a interação era efetuada externamente ao jogo, através de um fórum de mensagens, o que demonstrou ser pouco motivante para os jogadores, pois muitos deles nem se aperceberam que havia uma componente de colaboração no jogo. O objetivo desta tese incide sobre estes dois problemas, e consistiu em desenvolver um editor e motor de jogo com uma interface simples de utilizar, que não necessita de conhecimentos prévios de programação, e que permite criar jogos de aventura gráfica com uma componente de colaboração online verdadeiramente embebida na jogabilidade. A aplicação desenvolvida foi testada por um conjunto de utilizadores de diversas áreas, tendo-se obtido resultados que demonstram a acessibilidade e simplicidade da mesma, independentemente do nível de experiência prévio de programação do utilizador. A componente de colaboração online foi também muito bem recebida pelos utilizadores, os quais demonstraram bastante interesse em ver jogos de aventura gráfica com componente de colaboração online serem desenvolvidos no futuro.

The emergence of a curriculum model for online learning in a context of late investment in distance education

ECER 2015 "Education and Transition - Contributions from Educational Research", Corvinus University of Budapest from 7 to 11 September 2015.

A new horizon for online teaching and learning

The Polytechnic Institute of Oporto (IPP), which has a solid history of online education and innovation through the use of technology, has been particularly interested and focused on Massive Open Online Courses (MOOC) developments. The aim of this paper is to present the whole process from initial discussions to completion of the “Mathematics Without Limits” MOOC Project that exists in IPP and also to contribute for a change in the way as teaching and learning Mathematics is seen and practiced nowadays. In 2013, IPP developed its own platform, which gave us the opportunity to explore new educational techniques as a pedagogical resource as well as to enhance students’ motivation, through a set of interactive materials at their disposal, totally adapted to their needs. Students lack of motivation is mainly justified by their weak Math preparation, poor consolidated basis on the subject and different backgrounds of the students. To tackle this issue and based on our Math online courses teaching experience, we decided to create short duration MOOC, expecting to aid retention of students and also to reverse the path of students giving up on Math by giving them a friendly way of managing their own learning commitment. We also think that this MOOC will be a good approach to level out some math skills among freshmen.

A new horizon for online teaching and learning

The Polytechnic Institute of Oporto (IPP), which has a solid history of online education and innovation through the use of technology, has been particularly interested and focused on Massive Open Online Courses (MOOC) developments. The aim of this paper is to present the whole process from initial discussions to completion of the “Mathematics Without Limits” MOOC Project that exists in IPP and also to contribute for a change in the way as teaching and learning Mathematics is seen and practiced nowadays. In 2013, IPP developed its own platform, which gave us the opportunity to explore new educational techniques as a pedagogical resource as well as to enhance students’ motivation, through a set of interactive materials at their disposal, totally adapted to their needs. Students lack of motivation is mainly justified by their weak Math preparation, poor consolidated basis on the subject and different backgrounds of the students. To tackle this issue and based on our Math online courses teaching experience, we decided to create short duration MOOC, expecting to aid retention of students and also to reverse the path of students giving up on Math by giving them a friendly way of managing their own learning commitment. We also think that this MOOC will be a good approach to level out some math skills among freshmen.

A New horizon for online teaching and learning

The Polytechnic Institute of Oporto (IPP), which has a solid history of online education and innovation through the use of technology, has been particularly interested and focused on Massive Open Online Courses (MOOC) developments. The aim of this paper is to present the whole process from initial discussions to completion of the “Mathematics Without Limits” MOOC Project that exists in IPP and also to contribute for a change in the way as teaching and learning Mathematics is seen and practiced nowadays. In 2013, IPP developed its own platform, which gave us the opportunity to explore new educational techniques as a pedagogical resource as well as to enhance students’ motivation, through a set of interactive materials at their disposal, totally adapted to their needs. Students lack of motivation is mainly justified by their weak Math preparation, poor consolidated basis on the subject and different backgrounds of the students. To tackle this issue and based on our Math online courses teaching experience, we decided to create short duration MOOC, expecting to aid retention of students and also to reverse the path of students giving up on Math by giving them a friendly way of managing their own learning commitment. We also think that this MOOC will be a good approach to level out some math skills among freshmen.