Browsing by Subject "active learning"
Now showing items 1-4 of 4
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(2017)The study identifies the relationships between active learning, student teachers’ self-regulated learning and professional competences. Further, the aim is to investigate how active learning promotes professional competences of student teachers with different self-regulation profiles. Responses from 422 student teachers to an electronic survey were analysed using statistical methods. It was found that the use of active learning methods, such as goal-oriented and intentional learning as well as autonomous and responsible group work, are strongly and positively related to the achievement of professional competences. To develop the best competences, student teachers need high learning motivation and excellent self-regulation strategies. The mean scores in professional competences of highly motivated student teachers with excellent self-regulated learning were significantly higher when their experiences of active learning increased. Moreover, student teachers with high motivation and moderate self-regulation also benefited significantly from the use of active learning methods.
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(2018)Background. This article reports on pedagogical experiences of designing and teaching an active learning international relations (IR) course utilizing the classical board game DIPLOMACY, with added game elements and modified game rules to make the game better suited for educational purposes. Aim. Game adaptations include team play, a dedicated peace mediator team, altered win rules and a post-game debriefing discussion on different cultures of anarchy. These elements were designed to overcome a shortcoming that the game approximates a worldview akin to offensive realism, which is not practical in contemporary international relations, and also normatively objectionable to many IR scholars. Method. Teacher experiences designing and modifying the course, coupled with student feedback on the course concept from three consecutive years. Results. Student feedback has been exceedingly positive, with a 4.61 average grade (n = 210 grades) on a five-point Likert-type scale, where 1 signifies poor and 5 excellent. Conclusions. Through game modifications, students turned a game infamous for its backstabbing and breaking of promises into a game that resolves in a mediated and negotiated outcome. The findings suggest that DIPLOMACY can be useful beyond teaching the realist worldview, and adapted to create a more accurate microworld approximation of international relations.
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(2022)When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For cases when there are no annotations available, one approach is to utilize the structure of the feature space for clustering-based active learning (AL) methods. However, these methods are heavily dependent on how the samples are organized in the feature space and what distance metric is used. Unsupervised methods such as contrastive predictive coding (CPC) can potentially be used to learn organized feature spaces, but these methods typically create high-dimensional features which might be challenging for estimating data density. In this paper, we combine CPC and multiple dimensionality reduction methods in search of functioning practices for clustering-based AL. Our experiments for simulating speech emotion recognition system deployment show that both the local and global topology of the feature space can be successfully used for AL, and that CPC can be used to improve clustering-based AL performance over traditional signal features. Additionally, we observe that compressing data dimensionality does not harm AL performance substantially, and that 2-D feature representations achieved similar AL performance as higher-dimensional representations when the number of annotations is not very low.
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(Helsingin yliopisto, 2022)Two factors define the success of a deep neural network (DNN) based application; the training data and the model. Nowadays, many state-of-the-art DNN models are available free of charge, and training and deploying these models is easier than ever before. As a result, anyone can set up a state-of-the-art DNN algorithm within days or even hours. In the past, most of the focus has been given to the model when researchers were building faster and more accurate deep learning architectures. These research groups commonly use large and high-quality datasets in their work, which is not the case when one wants to train a new model for a specific use case. Training a DNN algorithm for a specific task requires collecting a vast amount of unlabelled data and then labeling the training data. To train a high-performance model, the labeled training dataset must be large and diverse to cover all relevant scenarios of the intended use case. This thesis will present an efficient and straightforward active learning method to sample the most informative images to train a powerful anchor-free Intersection over Union (IoU) predicting objector detector. Our method only uses classification confidences and IoU predictions to estimate the image informativeness. By collecting the most informative images, we can cover the whole diversity of the images with fewer human-annotated training images. This will save time and resources, as we avoid labeling images that would not be beneficial.
