Active Learning: New Approaches, and Industrial Applications

In the present scenario of education system, all people are under pressure to use the innovative strategies in the teaching and learning process, to teach students the knowledge and skills that are required for the 21st century. Active learning is a term used to identify teaching methods that require students to be actively involved in the learning process. Although there is clear evidence for the benefits of active learning, most lecturers in higher education still adhere to traditional teaching methods. This paper seeks to identity the characteristics and attitudes of “active instructors” towards active learning and discerning a distinction between these attitudes and those of the remaining instructors. This study examined the attitudes of 160 lecturers in three higher education institutions in East Ethiopia. The research used attitude questionnaire developed specially for this study on the basis of the experience of 7 ‘’active instructors” exposing the process of change they had undergone moving from traditional teaching to more active instruction. An analysis of these interviews provided the basis for characterizing the attitudes of “active instructors” and subsequently for the development of the research questionnaire. Based on a literature review and an examination of the attitudes of ‘’active instructors’’ a content analysis was undertaken in which the attitudes were grouped into six key domains that can characterize the tendency of a lecturer to adopt active teaching. The findings reveal that in all these six domains there were differences between the attitudes of “instructors” and their colleagues. This diagnostic tool can supply crucial information to the college and universities directors when planning supportive steps toward advancing active learning in their institutions. Key words: Active learning, instructors’ attitudes, traditional learning versus active learning, scale-up, innovation.

Active learning has been a research area conducted across various domains for a long time, from traditional machine learning to the latest deep learning research. Particularly, obtaining high-quality labeled datasets for supervised learning requires human annotation, and an effective active learning strategy can greatly reduce annotation costs. In this study, we propose a new insight, CFP-AL (Combining model Features and Prediction for Active Learning), from the perspective of feature space by analyzing and diagnosing methods that have shown good performance in NLP (Natural Language Processing) sentence classification. According to our analysis, while previous active learning strategies that focus on finding data near the decision boundary to facilitate classifier tuning are effective, there are very few data points near the decision boundary. Therefore, a more detailed active learning strategy is needed beyond simply finding data near the decision boundary or data with high uncertainty. Based on this analysis, we propose CFP-AL, which considers the model’s feature space, and it demonstrated the best performance across six tasks and also outperformed others in three Out-Of-Domain (OOD) tasks. While suggesting that data sampling through CFP-AL is the most differential classification standard, it showed novelty in suggesting a method to overcome the anisotropy phenomenon of supervised models. Additionally, through various comparative experiments with basic methods, we analyzed which data are most beneficial or harmful for model training. Through our research, researchers will be able to expand into the area of considering features in active learning, which has been difficult so far.

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.

Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.

One of the hot topics in education in the past 10 years has been the shift of the role of the educator. Whereas, he has traditionally been the owner and deliverer of the knowledge (Sage on the stage), now his role is shifting to a guide and facilitator (guide by the side). The purpose is to give the students ownership in their own learning process. As technology becomes more sophisticated, automation is replacing students’ problem solving skills, critical thinking and sometimes patience. On one of my evaluations in a 1999 online course, a student criticized that, “she’s not doing the teaching, I’m doing the learning.” Of course in my desire to encourage active learning, I took the response as a compliment, but the student meant it as a criticism. I began pondering the reluctance of students to take control of the learning process.

An overview of this dissertation is provided in Chapter 1, where the concept of active learning is introduced and the main idea of the proposed method is explained at a high level. The defining mechanism of the method can be summarized as bias-correction for stable algorithms. Practically, we take advantage of the nice properties of the random forest algorithm to do active learning in a computationally economical way. In Chapter 2, we rigorously establish that random forests are stable under mild conditions. This result not only serves as the theoretical foundation of our method, but also provides coverage guarantees to the prediction intervals constructed from the out-of-bag error of random forests. In Chapter 3, the proposed active learning method is introduced in detail. Its performance on real datasets is illustrated and compared with other methods. In particular, we show that our bias-oriented method and an existing variance-oriented method are complementary to each other. In Chapter 4, we address the issue of constructing prediction intervals for active learning. If a validation set is available, we can modify the split conformal prediction method to achieve this goal with guarantees. Otherwise, we can still use the calibrated out-of-bag error to construct prediction intervals with a small coverage gap. We summarize the dissertation and discuss some future work in Chapter 5.

In many human activity recognition systems the size of the unlabeled training data may be significantly large due to expensive human effort required for data annotation. Moreover, the insufficient data collection process from heterogenous sources may cause dissimilarities between training and testing data. To address these limitations, a novel probabilistic approach that combines learning using privileged information (LUPI) and active learning is proposed. A pool-based privileged active learning approach is presented for semi-supervising learning of human activities from multimodal labeled and unlabeled data. Both uncertainty and distance from the decision boundary are used as query inference strategies for selecting an unlabeled observation and querying its label. Experimental results in four publicly available datasets demonstrate that the proposed method can identify complex human activities with high accuracy.

Teaching English to non-native speakers needs to incorporate up-to-date techniques and strategies that are frequently updated to match the ever-changing needs of modern society. In order for students to start acquiring English skills effectively inside the classroom, they need to be more active in their learning and be ready to take the initiative. For this to happen, lecturers need to adopt techniques that facilitate “active learning” with the suitable environment for students to make better use of their innate abilities and become active learners. In a classroom environment where active learning is prominent, students take responsibility of their learning of English as a second language with the lecturer acting as facilitator whose role is limited to providing instructions and supervising the process. Students will have more self-confidence to express their opinions and actively participate in topic discussions. This paper discusses methods of active learning that can be applied in teaching English at Cihan University.

The purpose of this study is to find out the active learning application based on project assignment in improving science learning results of students in VII B class of SMP Kristen 2 Salatiga and to find out the improvement of the science learning results of students in VII B class of SMP Kristen 2 Salatiga throuh applying the active learning based on the project assignment. This study was done in SMP Kristen 2 Salatiga by using 21 students in this school as the subjects. The method applied in this study is the descriptive analysis method with the data collection instruments such as questionnaires, pbservation sheets and interview. The results of this study show that the active learning model based on the project assignment could improve students’ cognitive, psychomotor and affective learning results. Based on the students’ cognitive learning results, the students’ percentage fulfilling the minimum criteria (KKM) in cycle 1 is 66.67% and it improves into 90.47% in cycle 2. Based on the students’ psychomotor learning results, the students’ percentage fulfilling the minimum criteria (KKM) with a good criterion (≥ 25) in cycle 1 is 76.19% and it improves into 100% in cycle 2. Based on the students’ affective learning results, the students’ percentage fulfilling the minimum criteria (KKM) with a good criterion (≥ 25) in cycle 1 is 80.95% and it improves into 100% in cycle 2. Keywords : Active Learning Based on Project Assignment, Learning Results

This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged into other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.

BackgroundCytokines act by binding to specific receptors in the plasma membrane of target cells. Knowledge of cytokine–receptor interaction (CRI) is very important for understanding the pathogenesis of various human diseases—notably autoimmune, inflammatory and infectious diseases—and identifying potential therapeutic targets. Recently, machine learning algorithms have been used to predict CRIs. “Gold Standard” negative datasets are still lacking and strong biases in negative datasets can significantly affect the training of learning algorithms and their evaluation. To mitigate the unrepresentativeness and bias inherent in the negative sample selection (non-interacting proteins), we propose a clustering-based approach for representative negative sample selection.ResultsWe used deep autoencoders to investigate the effect of different sampling approaches for non-interacting pairs on the training and the performance of machine learning classifiers. By using the anomaly detection capabilities of deep autoencoders we deduced the effects of different categories of negative samples on the training of learning algorithms. Random sampling for selecting non-interacting pairs results in either over- or under-representation of hard or easy to classify instances. When K-means based sampling of negative datasets is applied to mitigate the inadequacies of random sampling, random forest (RF) together with the combined feature set of atomic composition, physicochemical-2grams and two different representations of evolutionary information performs best. Average model performances based on leave-one-out cross validation (loocv) over ten different negative sample sets that each model was trained with, show that RF models significantly outperform the previous best CRI predictor in terms of accuracy (+ 5.1%), specificity (+ 13%), mcc (+ 0.1) and g-means value (+ 5.1). Evaluations using tenfold cv and training/testing splits confirm the competitive performance.ConclusionsA comparative analysis was performed to assess the effect of three different sampling methods (random, K-means and uniform sampling) on the training of learning algorithms using different evaluation methods. Models trained on K-means sampled datasets generally show a significantly improved performance compared to those trained on random selections—with RF seemingly benefiting most in our particular setting. Our findings on the sampling are highly relevant and apply to many applications of supervised learning approaches in bioinformatics.

This paper introduces an active learning technique for relevance vector machines. One of the most used methods for training active learners makes use of support vector machines as classifiers. However, despite its success, several disadvantages can be advised in the support vector methodology. The novelty of the proposed method consists on keeping all of the advantages of active learning, but replacing support vector machines with another classifier that does not suffer of its disadvantages, aiming to obtain an active learning method even more robust. Results over standard and biosignal datasets show satisfactory and comparable performances of the proposed method in contrast to classic support vector machines active learning.

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

low student learning outcomes and lack of activity due to the students' learning in the learning process is more focused only on the teacher. Teachers do not fully engage students in learning. Students are more dominant as a receiver only so that students tend to passive learning. This will result in mathematics boring and unpleasant. The implication of activity and student learning was low. So that the learning process goes well it needs a setrategi active learning that engages students in the learning process that is expected to increase the activity and student learning outcomes. Therefore, this study was conducted to improve learning outcomes math class VII MTs Ar-rusydiny through the implementation of learning model of problem-based instruction.This type of research is a classroom action research conducted in two cycles. An instrument which is used mainly obserpasi and test sheets. From the results of research in proleh that the number of students in the activity scores 2 sisklus always increase. In the first cycle of activity percentage is 56.41% with the active category, while in the second cycle the percentage of student activity is 69, 23% to the category of active while classical completeness obtained 60% in the first cycle and 90% in the II.Hal cycle already addressed achievement research indicator that the set, so it can be concluded that through the implementation of learning model of problem-based instruction can improve students' mathematics learning outcomes kelasVII MTs Ar-rusydiny the school year 2016/2017

In recent years, machine learning has been vastly used in many scientific experiments to analyze and predict the data [1]. Since there are many types of machine learning and each of them has additional further branches, it's necessary to understand which one can predict or summarize the data with more accuracy. This paper mainly focuses on traditional machine learning and active learning. To compare the effects of these two types of machine learning, the paper uses a dataset about using metal oxide semiconductor (MOX) to measure the concentration of carbon monoxide (CO). In the first part, the paper tests the accuracy of predicting CO concentration with 3 types of traditional machine learning: Decision Tree, Random Forest and K-nearest neighbors. In the second part, the paper chooses the traditional machine learning which has the best performance in the first part and compares the accuracy of it with the active learning on predicting CO concentration. The result is that the classification accuracy of Random Forest is 0.625, which is the highest among them. And active learning is generally better than traditional machine learning when training samples are small, and they have a similar accuracy when the training sample is enough.