Beyond Disagreement-based Agnostic Active Learning

We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor

Sampling with Confidence: Using k-NN Confidence Measures in Active Learning

Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures in a new active learning sampling selection strategy, and shows how the performance of this strategy is better than one based on uncertainty sampling using classification scores

Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

Faculty Beliefs on Active learning Strategies in Higher Education: Identification of Predictors for Use of Active Learning

Evaluating the perceptions of active learning strategies is often seen from the perspective of the learners at the primary and secondary education levels. Additional data on beliefs of active learning in higher education such as of faculty members is needed. Active learning strategies are on the front line in education as a method to enhance student learning and foster twenty-first century skills. Developing twenty-first century skills is essential as the environment of the workplace is dynamic and evolving requiring individuals to rely on critical thinking and diverse application of their knowledge. Universities should continue to evolve to best prepare graduates for their endeavors postgraduation. Gaining an understanding on beliefs of active learning in higher education is beneficial as it provides insight into the faculty beliefs and how to foster a culture promoting twenty-first century skills. This study sought to understand faculty beliefs on active learning strategies and use of active learning in higher education. Faculty from three southeast universities were surveyed and a total of 210 participants completed the survey. Data was collected and analyzed to determine variables that were predictors of the frequency of use of active learning strategies and overall use of active learning strategies. The study found an overall high frequency of use of active learning strategies (M = 3.82, SD = .81), confidence in using active learning strategies (M = 3.95, SD = .84), and job satisfaction (M = 3.99, SD = .73). Correlations for frequency of use of active learning included beliefs on learning with a positive correlation of (.43), professional development with a positive correlation of (.34), and confidence in use of active learning strategies with a positive correlation of (.68). Correlations for overall use of active learning strategies included confidence in use with a positive correlation of (.38), beliefs with a positive correlation of (.36), and professional development with a positive correlation of (.26). Logistical barriers were found to be negatively correlated to both frequency of use (r =-.39) and overall use of active learning (r = -.34). The most prevalent barrier to the use of active learning was that faculty were not trained how to use these strategies. The most prevalent active learning strategy used was Socratic questioning. Regression analysis identified several predictor variables to the frequency of use of active learning strategies and for the overall use of presented active learning strategies. The predictor variables having a positive influence included beliefs on learning (a more constructivist viewpoint), professional development, and confidence in use of active learning. In addition, level of course undergraduate (lower-level courses indicating more active learning) positively predicted the frequency of use of active learning strategies. This study provided insight into the belief set of faculty members as well as the barriers seen by the faculty. The results from this study can provide universities insight to develop programs and provide support and training to their faculty to aid in their teaching and fostering of student learning. Several avenues for future research were identified and presented in the study to continue gaining insight into the beliefs of faculty member on active learning, barriers to active learning, and potential solutions to barriers

Elements in Scenario-Based Simulation Associated with Nursing Students’ Self-Confidence and Satisfaction: A Cross-Sectional Study

Aim: To identify elements in scenario‐based simulation associated with nursing students' satisfaction with the simulation activity and self‐confidence in managing the simulated patient situation. The study will provide insight to improve the use of simulation as a learning strategy. Design: A cross‐sectional study. Method: The Student Satisfaction and Self‐Confidence in Learning scale was used as the outcome measure to identify associations with elements of the Simulation Design Scale and the Educational Practices Questionnaire scale after scenario‐based simulation using patient simulators. First‐year nursing students at a university college in Norway (N = 202) were invited to participate and (N = 187) responded to the questionnaires. Results: The mean scores for self‐confidence and satisfaction were 4.16 and 4.57, respectively. In the final multiple linear regression analysis, active learning was associated with satisfaction with the simulation activity, while clear objectives and active learning were associated with self‐confidence in managing the simulated patient situation.publishedVersio

Cooperative Learning and its Application to Emotion Recognition from Speech

In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of human annotation. The core underlying idea of Cooperative Learning is to share the labeling work between human and machine efficiently in such a way that instances predicted with insufficient confidence value are subject to human labeling, and those with high confidence values are machine labeled. We conducted various test runs on two emotion recognition tasks with a variable number of initial supervised training instances and two different feature sets. The results show that Cooperative Learning consistently outperforms individual Active and Semi-Supervised Learning techniques in all test cases. In particular, we show that our method based on the combination of Active Learning and Co-Training leads to the same performance of a model trained on the whole training set, but using 75% fewer labeled instances. Therefore, our method efficiently and robustly reduces the need for human annotations

Prestasi Belajar Matematika Ditinjau dari Kepercayaan Diri dan Keaktifan Siswa di Kelas

. The purpose of the study was to analyze whether there is influence confidence and activity of students in the class of the mathematics achievement class VIII SMP Negeri 208 Jakarta. The research method in this study is a survey method through correlation techniques. The population in this study were all students of class VIII SMP 208. Sampling techniques using random sampling techniques,. The amount of a sample group of 40 students. Confidence data collection instruments, and active participation by students in class with each of 30 questionnaires and data collection Mathematics Learning Achievement through documents UAS value. Then test data requirements that normality test with chi-square test, which marks the third data Xh <Xt, namely Confidence (5.65 <7.815), active participation by students in Grades (1.42 <7.815), mathematics Learning Achievement (0, 39 <7.815), then three normal distribution. The next test of linearity regression with ? = 0.05. Mathematics Learning Achievement on Self Confidence Fh <Ft (1.015 <2.35), then H0 is accepted concluded subsequent linear regression model patterned Mathematics Learning Achievement of the activeness of students in Grades Fh <Ft (0.4753 <2.13) H0 accepted, patterned linear regression model. Hypothesis testing technique used is the technique of double correlation. Double correlation test with ? = 0.05, then to DK1 = 2 and obtained Ftabel dk2 = 37 = 3.25. Because Fh> Ft (6.87 <3.25) is high, we conclude that together a significant difference between Confidence and active participation by students in Grades towards mathematics achievemen