Self-directed learning, lifelong learning and transformative learning in the Society for Creative Anachronism

This study examines the connection between leisure group participation and learning activities undertaken by participants in the Society for Creative Anachronism (SCA), a medieval recreationist group. The thesis of this connection was developed through the researcher's observations during SCA participation. The intent of this study is to understand adult learning from the self-directed learning, lifelong learning, and -transformative learning components derived from participant's SCA experiences. This qualitative study was conducted by interviewing eight active SCA participants, two in each participation theme of historical research, artistic representation, performance, and martial skills. Informants' responses demonstrated an integration of their leisure activity with learning. The contextualization of learning a s both a primary activity and a necessary support to participation, places learning a t the heart of participants' SCA related activities. The positive descriptions of learning activities, descriptive terms of ownership, and situating learning as an enjoyable activity engaged for the pleasure of the experience, provides adult educators with a fascinating glimpse of willing and engaged adult learners pursuing lifelong learning outside of the traditional educational structure. Two themes emerged during the interviews. First, bonding with others provided the motivation to continue their activities. Secondly, a feeling of commitment and helonging defined their enjoyment and satisfaction with SCA participation. The clear implications are that adult educators can create effective learning communities by developing educational structures that engage adult learners wi th meaningful social interaction.

Self-Regulated Learning and Children At-Risk for Learning Disabilities: Using Motivational Tactics to Support "Reading Rocks!"

This project explored self-regulation among children impacted by leaming disabilities. More specifically, this thesis examined whether a remedial literacy program called Reading Rocks! offered by the Leaming Disabilities Association of Niagara Region, provided participating children opportunities to set goals, develop strategies to meet these goals, and provide intemal and extemal feedback- all processes associated with a model of self-regulated leaming as pioneered by Butler and Winne (1995) and Winne and Hadwin (1999). In this thesis, I triangulate the data through the combination of three different methodologies. Firstly, I describe the various elements of the Reading Rocks! program. Secondly, I analyze the data gathered through three semi-structured interviews with three parents of children that participated in the Reading Rocks! program to demonstrate whether the program provides opportunities for children to self-regulate their learning. Thirdly, I also analyze photographic evidence of the motivational workstation boards created by the tutors and children to further illustrate how Reading Rocks! promotes self-regulatory processes among children.

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Implementation of training programs in self-regulated learning strategies in Moodle format : Results of a experience in higher education

Resumen tomado de la publicaci??n

Assessing self-regulated learning in early childhood education : Difficulties, needs, and prospects

Resumen tomado de la publicaci??n

New media for the promotion of self-regulated learning in higher education

Resumen tomado de la publicaci??n

Regulation and self-regulation learning. 'Regulación y autoregulación de los aprendizajes'.

Este vídeo nos enseña cómo se produce el proceso de regulación del aprendizaje por el profesor y nos muestra un método alternativo: la propia autoregulación del alumno.

Selecting valuable information to remember: age-related differences and similarities in self-regulated learning

It is often necessary to selectively attend to important information, at the expense of less important information, especially if you know you cannot remember large amounts of information. The present study examined how younger and older adults select valuable information to study, when given unrestricted choices about how to allocate study time. Participants were shown a display of point values ranging from 1–30. Participants could choose which values to study, and the associated word was then shown. Study time, and the choice to restudy words, was under the participant's control during the 2-minute study session. Overall, both age groups selected high value words to study and studied these more than the lower value words. However, older adults allocated a disproportionately greater amount of study time to the higher-value words, and age-differences in recall were reduced or eliminated for the highest value words. In addition, older adults capitalized on recency effects in a strategic manner, by studying high-value items often but also immediately before the test. A multilevel mediation analysis indicated that participants strategically remembered items with higher point value, and older adults showed similar or even stronger strategic process that may help to compensate for poorer memory. These results demonstrate efficient (and different) metacognitive control operations in younger and older adults, which can allow for strategic regulation of study choices and allocation of study time when remembering important information. The findings are interpreted in terms of life span models of agenda-based regulation and discussed in terms of practical applications. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract)

Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.