Search results for "e learning"
showing 10 items of 2703 documents
Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms
2020
Computer-supported collaborative inquiry-based learning (CSCIL) represents a form of active learning in which students jointly pose questions and investigate them in technology-enhanced settings. Scaffolds can enhance CSCIL processes so that students can complete more challenging problems than they could without scaffolds. Scaffolding CSCIL, however, would optimally adapt to the needs of a specific context, group, and stage of the group's learning process. In CSCIL, the stage of the learning process can be characterized by the inquiry-based learning (IBL) phase (orientation, conceptualization, investigation, conclusion, and discussion). In this presentation, we illustrate the potential of a…
Combining feature extraction and expansion to improve classification based similarity learning
2017
Abstract Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the data format used to learn the model. We then present an Enriched Classification Similarity Learning method that follows a hybrid approach that combines both feature extraction and feature expansion. In particular, we propose a data transformation and the use of a set of standard distances to supplement the information provided by the feature vectors of the training samples. The method is compared to state-of-the-art feature extraction and metric lear…
Emotions in Learning at Work : a Literature Review
2019
The research elaborating emotions in organizational settings has increased considerably in recent years. However, we lack a comprehensive understanding of the role of emotions in learning at work. This review aimed to elaborate how emotions and learning are understood in the field of workplace studies, and how emotions and learning at work are related. For the review, 31 scientific articles were selected and analysed. We found that emotions and learning were understood in a range of ways in the articles. Emotions were mainly defined as emotional experiences and responses, and learning at work mainly referred to learning through participatory practices. In addition, the review illustrates th…
A matter of perspective : the cognitive style Field Independence - dependence, and why it matters
2016
Psykologi Herman Witkin aloitti vuonna 1948 kognitiivista ajattelutapaa tutkivan suuntauksen, jossa ihmiset jaettiin kahteen ryhmään: kenttäriippuvaisiin ja kenttäriippumattomiin. Kenttäriippumattomuutta ja -riippuvaisuutta on tutkittu aina 2010 luvulle saakka. Kansainvälisiä tutkimustuloksia löytyy aiheeseen liittyen runsaastikin, mutta Suomessa aihetta ei ole juurikaan tutkittu soveltavan kielitieteen näkökulmasta. Tutkimukset kenttäriippuvaisuudesta ja -riippumattomuudesta antavat viitteitä siihen, että tällä kognitiivisen ajattelun mallilla saattaa olla suurikin vaikutus oppilaiden oppimismenestykseen. Kenttäriippumattomat oppilaat ovat aiempien tutkimusten mukaan itsenäisempiä, havaits…
Foetal ECG recovery using dynamic neural networks
2002
Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coe…
A Novel Multi-step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
2020
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata (TA) to a Nash Equilibrium of the TM game. In this paper, we propose a novel finite-state learning automaton that can replace the TA in TM learning, for increased determinis…
Valuing Variability: Dynamic Usage-based Principles in the L2 Development of Four Finnish Language Learners
2020
The general aim of this study is to trace the second language (L2) development of four beginner learners of Finnish over one academic year from a dynamic usage-based perspective. Contrary to many previous studies, this study starts out from meanings, not forms. In other words, an onomasiological approach is adopted. The aim is to investigate what kind of constructions the learners use to express 1) evaluation and 2) existentiality. In line with a dynamic usage-based approach, the goal is to investigate three aspects of development: 1) the interaction between different linguistic means used to express a certain meaning and between the instruction and learning trajectories, 2) variability pat…
Predicting Heuristic Search Performance with PageRank Centrality in Local Optima Networks
2015
Previous studies have used statistical analysis of fitness landscapes such as ruggedness and deceptiveness in order to predict the expected quality of heuristic search methods. Novel approaches for predicting the performance of heuristic search are based on the analysis of local optima networks (LONs). A LON is a compressed stochastic model of a fitness landscape's basin transitions. Recent literature has suggested using various LON network measurements as predictors for local search performance.In this study, we suggest PageRank centrality as a new measure for predicting the performance of heuristic search methods using local search. PageRank centrality is a variant of Eigenvector centrali…
Replacing radiative transfer models by surrogate approximations through machine learning
2015
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. They are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We here present an ‘Emulator toolbox’ that enables analyzing three multi-output machine learning regress…
On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI
2021
Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (…