Learning with MOOCs: promises and problems

Massive Open Online Courses (MOOCs) may be considered to be a new form of virtual technology enhanced learning environments. Since their first appearance in 2008, the increase in the number of MOOCs has been dramatic. The hype about MOOCs was accompanied by great expectations: 2012 was named the Year of the MOOCs and it was expected that MOOCs would revolutionise higher education. Two types of MOOCs may be distinguished: cMOOCs as proposed by Siemens, based on his ideas of connectivism, and xMOOCs developed in institutions such as Stanford and MIT. Although MOOCs have received a great deal of attention, they have also met with criticism. The time has therefore come to critically reflect upon this phenomenon.

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.

Voluntariado online : construção, partilha e descoberta

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

Voluntariado online : construção, partilha e descoberta

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

‘‘Model age-based” and ‘‘copy when uncertain” biases in children’s social learning of a novel task

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of mode

The development of selective copying: children’s learning from an expert versus their mother

This study tested the prediction that, with age, children should rely less on familiarity and more on expertise in their selective social learning. Experiment 1 (N=50) found that 5- to 6-year-olds copied the technique their mother used to extract a prize from a novel puzzle box, in preference to both a stranger and an established expert. This bias occurred despite children acknowledging the expert model’s superior capability. Experiment 2 (N=50) demonstrated a shift in 7-to 8-year-olds towards copying the expert. Children aged 9- to 10-years did not copy according to a model bias. The findings of a follow-up study (N=30) confirmed that, instead, they prioritized their own – partially flawed – causal understanding of the puzzle box.

Socratic flipped classroom : What types of questions and tasks promote learning?

Socratic questioning stresses the importance of questioning for learning. Flipped Classroom pedagogy generates a need for effective questions and tasks in order to promote active learning. This paper describes a project aimed at finding out how different kinds of questions and tasks support students’ learning in a flipped classroom context. In this study, during the flipped courses, both the questions and tasks were distributed together with video recordings. Answers and solutions were presented and discussed in seminars, with approximately 10 participating students in each seminar. Information Systems students from three flipped classroom courses at three different levels were interviewed in focus groups about their perceptions of how different kinds of questions and tasks supported their learning process. The selected courses were organized differently, with various kinds of questions and tasks. Course one included open questions that were answered and presented at the seminar. Students also solved a task and presented the solution to the group. Course two included open questions and a task. Answers and solutions were discussed at the seminars where students also reviewed each other’s answers and solutions. Course three included online single- and multiple choice questions with real-time feedback. Answers were discussed at the seminar, with the focus on any misconceptions. In this paper we categorized the questions in accordance with Wilson (2016) as factual, convergent, divergent, evaluative, or a combination of these. In all, we found that any comprehensible question that initiates a dialogue, preferably with a set of Socratic questions, is perceived as promoting learning. This is why seminars that allow such questions and discussion are effective. We found no differences between the different kinds of Socratic questions. They were seen to promote learning so long as they made students reflect and problematize the questions. To conclude, we found that questions and tasks promote learning when they are answered and solved in a process that is characterized by comprehensibility, variation, repetition and activity.

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).

Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.

End-to-end Deep Metric Learning con Vision-Language Model per il Fashion Image Captioning

L'image captioning è un task di machine learning che consiste nella generazione di una didascalia, o caption, che descriva le caratteristiche di un'immagine data in input. Questo può essere applicato, ad esempio, per descrivere in dettaglio i prodotti in vendita su un sito di e-commerce, migliorando l'accessibilità del sito web e permettendo un acquisto più consapevole ai clienti con difficoltà visive. La generazione di descrizioni accurate per gli articoli di moda online è importante non solo per migliorare le esperienze di acquisto dei clienti, ma anche per aumentare le vendite online. Oltre alla necessità di presentare correttamente gli attributi degli articoli, infatti, descrivere i propri prodotti con il giusto linguaggio può contribuire a catturare l'attenzione dei clienti. In questa tesi, ci poniamo l'obiettivo di sviluppare un sistema in grado di generare una caption che descriva in modo dettagliato l'immagine di un prodotto dell'industria della moda dato in input, sia esso un capo di vestiario o un qualche tipo di accessorio. A questo proposito, negli ultimi anni molti studi hanno proposto soluzioni basate su reti convoluzionali e LSTM. In questo progetto proponiamo invece un'architettura encoder-decoder, che utilizza il modello Vision Transformer per la codifica delle immagini e GPT-2 per la generazione dei testi. Studiamo inoltre come tecniche di deep metric learning applicate in end-to-end durante l'addestramento influenzino le metriche e la qualità delle caption generate dal nostro modello.

Supporting law students’ skills development online – a strategy to improve skills and reduce student stress?

Law students internationally suffer from a high level of psychological distress compared with the general and student populations, and anecdotal evidence suggests that students developing skills without adequate support experience significant stress and anxiety. This article considers an initiative at one Australian law school to develop a degree-wide structured online skills development programme as a means to both improve student skills acquisition and reduce student stress. The project implements, through the use of learning technology, the principles proposed by McKinney for making small changes to law school teaching, informed by self-efficacy theory, which can have powerful results.

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.

Best-practice model for technology enhanced learning in the creative arts

This paper presents a best-practice model for the redesign of virtual learning environments (VLEs) within creative arts to augment blended learning. In considering a blended learning best-practice model, three factors should be considered: the conscious and active human intervention, good learning design and pedagogical input, and the sensitive handling of the process by trained professionals. This study is based on a comprehensive VLE content analysis conducted across two academic schools within the creative arts at one Post-92 higher education (HE) institution. It was found that four main barriers affect the use of the VLE within creative arts: lack of flexibility in relation to navigation and interface, time in developing resources, competency level of tutors (confidence in developing online resources balanced against other flexible open resources) and factors affecting the engagement of ‘digital residents’. The experimental approach adopted in this study involved a partnership between the learning technology advisor and academic staff, which resulted in a VLE best-practice model that focused directly on improving aesthetics and navigation. The approach adopted in this study allowed a purposive sample of academic staff to engage as participants, stepping back cognitively from their routine practices in relation to their use of the VLE and questioning approaches to how they embed the VLE to support teaching and learning. The model presented in this paper identified a potential solution to overcome the challenges of integrating the VLE within creative arts. The findings of this study demonstrate positive impact on staff and student experience and provide a sustainable model of good practice for the redesign of the VLE within creative disciplines.

Blended Learning Essentials Project reports

Blended Learning Essentials is a free suite of online courses for the Vocational Education and Training sector to promote effective practice and pedagogy in blended learning. The courses were run and supported from 2016 onwards by a consortium of partners funded by Ufi Charitable Trust. The lead partners were the University of Leeds, the UCL Institute of Education, the Association for Learning Technology (ALT), and FutureLearn. The Blended Learning Essentials (BLE) courses are for anyone working in further education, skills training, vocational education, workplace learning, lifelong learning or adult education, who wants to learn about and implement blended learning. The project reports cover engagement and marketing work undertaken during this project phase to reach the courses’ key audiences and work undertaken during this project phase to develop and promote the pathways to accreditation available to course participants. These reports are shared by ALT as a project partner on behalf of the BLE Project under a CC-BY-NC-ND licence. �