Domain Adaptation by Active Learning

The world is changing so fast, and therefore education needs to adapt to the challenges of times. In order to update the content of school education in the Republic of Kazakhstan modern trends are going to be used. These trends contain pedagogical methods that can be used to preserve and even increase internal motivation, as active learning. Active learning method is an treatment where students participate or interact with the learning process, as opposed to passively taking in the information.The goal of this study is to identify the impact of active learning method on 10th grade students’ attitude towards mathematics of the students the second semester of the school year 2019-2020. More specifically, it attempted to determine and compare the attitude toward mathematics of students’ exposure to active learning and traditional teaching strategy. The Likert scale used to evaluate the attitude of students toward mathematics. Mean, Cronbach value, T-test were the statistical tools used in anatomizing and interpreting the research data. The discovering showed that the students in the active learning group had auspicious attitude than students in the conventional teaching group. According to the findings after research, we saw the direct relation between attitude and active learning. It is concluded that the students’ attitude toward mathematics was better by using active learning strategy. It is recommended that mathematics teacher should use active learning strategy in order to improve the attitude toward mathematics of the students.Keywords: attitude, mathematics, active learning

Supervised learning methods require sufficient labeled examples to learn a good model for classification or regression. However, available labeled data are insufficient in many applications. Active learning (AL) and domain adaptation (DA) are two strategies to minimize the required amount of labeled data for model training. AL requires the domain expert to label a small number of highly informative examples to facilitate classification, while DA involves tuning the source domain knowledge for classification on the target domain. In this paper, we demonstrate how AL can efficiently minimize the required amount of labeled data for DA. Since the source and target domains usually have different distributions, it is possible that the domain expert may not have sufficient knowledge to answer each query correctly. We exploit our active DA framework to handle incorrect labels provided by domain experts. Experiments with multimedia data demonstrate the efficiency of our proposed framework for active DA with noisy labels.

The COVID-19 pandemic created a paradigm shift in the way educators employ active learning strategies. In this chapter, the authors discuss how engaging and innovative learning strategies were developed to teach baccalaureate-level nursing students during the COVID-19 pandemic. The initial focus is on the teaching and learning strategies created for first-semester students who are developing foundational nursing skills and concepts. The discussion transitions to complex strategies developed for fourth-semester students, solidifying critical thinking and clinical judgment skills. Highlighted are active learning strategies used in the classroom, skills lab, and simulated clinical environment. These promote clinical judgment and present practical direction for adapting technology to provide an engaging learning environment. Throughout the chapter, the authors use several strategies to showcase how a nursing program responded to COVID-19 restrictions, including active learning and technology strategies, and how they can be applied across a curriculum using varying levels of technology.

Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network (DNN) mostly relies on a large number of labeled samples being available. To address this problem, this paper proposes a unified deep network, combined with active transfer learning that can be well-trained for HSIs classification using only minimally labeled training data. More specifically, deep joint spectral-spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks. Active transfer learning is then exploited to transfer the pre-trained SSAE network and the limited training samples from the source domain to the target domain, where the SSAE network is subsequently fine-tuned using the limited labeled samples selected from both source and target domain by corresponding active learning strategies. The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel active learning strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross-dataset and intra-image; 3) the learned deep joint spectral-spatial feature representation is more generic and robust than many joint spectral-spatial feature representation. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based methods, on three popular datasets.

Dual contrastive universal adaptation network for multi-source visual recognition

Although the relationship between student motivation, self-perception, and achievement in science education is well established, there is still a lack of focused studies on these aspects at the lower secondary level, particularly in private school settings where student demographics and learning environments may pose unique challenges. The purpose of this study is to: (1) assess lower secondary students' motivation and self-concept in scientific learning; and (2), using correlational analysis and qualitative feedback, determine ways to improve these aspects. Using a quantitative descriptive survey design, this study assessed 60 grade 7-9 students from a private lower secondary school. The Students’ Motivation Toward Science Learning (SMTSL) and adapted Chemistry Self-Concept Inventory (CSCI) measured motivation and self-concept, respectively. Data analysis included mean scores for both variables and Spearman’s rank correlation (accounting for Liker-scale ordinality and non-normal distributions). A moderate-high level of motivation (Active learning strategies [ALS]=4.0-4.1 highest, Performance Goal [PG]=2.8-3.1 lowest) and self-concept (Positive Perception of Science Self Concept [PSSC]=3.8-3.9 highest, Mathematic Self-Concept [MSC]=2.9-3.2 lowest) with moderate positive correlation (r = 0.572, p < 0.001) underscores that self-concept and motivation are interrelated, suggests interventions should simultaneously promote active learning (to enhance ALS) while addressing mathematical confidence (to improve MSC). These findings suggest that teachers can boost engagement through active learning and confidence-building strategies, while school and policymakers should design targete interventions to support students with low scientific confidence and high performance pressure.

PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

This study aimed at identifying the impact of using some active learning (AL)strategies in a Curricula and Teaching Methods Course on the learning motivation level and the development of Critical Thinking Skills among the Faculty of Arts at Al-Zaytoonah University (ZUJ) students. The study subjects were (61) male and female students selected from the Curricula and Teaching Methods Course of the Faculty of Arts at ZUJ. The study adopted the quasi-experimental approach. To answer the two questions of the study, the Learning Motivation Scale consisting of (35) paragraphs, and the Critical Thinking Skills Test consisting of (34) paragraphs were constructed. The study results revealed that there are significant differences between the means of the scores of the students of the Curricula and Teaching Methods course between the experimental group and the control group on the Learning Motivation Scale and the Critical Thinking Skills Test that can be attributed to using AL strategies. Therefore, the researcher recommended the use of learning outcomes related to learning motivation and critical thinking skills.

ABSTRACT: The COVID-19 pandemic and the recent technological advancements have given rise to the use of active, interactive, and innovative learning strategies in Higher Education in India that help in promoting collaborative learning. The aim of introducing flipped classroom strategy using some active learning strategies is to evaluate its effectiveness in use and academic performance, develop managerial, problem solving, creativity, and innovative skills in students, the need of the hour in the present situation where 80% of the engineering graduates are unemployable (aspiring minds report, 2019) and to arouse interest in the management courses which are given little emphasis by engineering students in their undergraduate program and which is affecting their placement because of the backlogs in Management courses. This strategy is used for the Management Science course, which is a common paper for all the engineering branches of III/IV B.Tech students. The results showed that the students were highly engaged in learning; interest was aroused in the subject, and there was drastic progress in academic performance, as well as improved problem-solving, creativity, managerial, and innovative skills.

Aim/Purpose: Two popular methods for encouraging active learning are Augmented Reality (AR) and Virtual Field Trips (VFTs). This exploratory case study examines college students’ perceptions of a prototype AR and VFT app as an active learning strategy. Background: AR allows students to learn as they physically explore a destination, while VFTs give students the opportunity to visit exciting destinations without leaving their homes. AR and VFTs promote active learning, which has been shown to increase college student success in Science, Technology, Engineering, and Math (STEM) courses. The aim of the VFT app in this study is to provide college students in a STEM course with an interactive lesson on modeling information systems using diagrams. Methodology: This exploratory case study is intended to serve as a condensed case study performed with the prototype version of a VFT app before implementing a large-scale investigation of students’ perceptions of a more refined version of the app. The study employed a qualitative approach involving a survey with open-ended questions to gather college students’ perceptions of learning with a VFT. The data were analyzed using inductive coding. The participants are students at a mid-sized, urban, public university. Contribution: This exploratory case study serves as a proof-of-concept and starting point for other faculty who may be interested in developing their own AR and VFT apps to engage students in active learning. Releasing the app to a common Open Educational Resources (OER) repository will give other faculty easy access to re-use the app and build upon it to create their own virtual field trips. OER are learning materials that are freely available for students and faculty to download and use in their coursework. Findings: Students overwhelmingly perceived the VFT app helped them learn about the subject that was presented, citing the visual nature of the app, the real-life scenarios presented in the app, and the app’s ease of use as reasons why. The majority (over 89%) also agreed that the app motivated them to learn more about the subject, mainly due to the app’s real-life scenarios, and over 83% of students cited at least one benefit to learning with the app, such as the navigation/location features, the easy-to-use interface, and the real-world scenarios. Recommendations for Practitioners: The pedagogical implications of this study are that faculty should adopt VFTs as an active learning strategy, particularly in STEM college courses, based on the students’ positive perceptions of learning, motivation, and benefits of VFTs. Recommendations for Researchers: Researchers can expand on this exploratory case study by conducting a larger-scale study of the VFT app employed in the case study, or by developing their own VFT app based on the one in this study, to capture a broader group of students’ perceptions of VFTs as an active learning strategy. Impact on Society: The broad impact of this research on society is encouraging the adoption of VFTs as an active learning strategy since active learning strategies are shown to increase college students’ success and engagement. Future Research: Future research will be conducted in subsequent terms to gather additional data on students’ perceptions of the VFT app, as well as their perceptions of the relationship between learning and the VFT technology. Further research is also needed to survey faculty on their perceptions of how engaging with the app impacts student learning, particularly in regards to the VFT technology within the app.

Existing meta-analyses have shown that active learning strategies are effective in improving students’ learning performance. However, their implementation may vary across school levels. This study investigated differences among elementary, middle, and high school teachers in the use of teaching strategies, including those that promote active learning and pedagogical beliefs. We also explored differences in the relationships between beliefs about teaching and learning, teaching experience, and teaching strategies for active learning across school levels. An online survey was conducted with 550 in-service elementary, middle, and high school teachers. Participants completed a questionnaire that measured their teaching strategies, beliefs about teaching and learning, and teaching experience. The results revealed differences in the frequency of use of six teaching strategies, including those that promoted active learning. Elementary school teachers used teaching strategies that promoted active learning most frequently, followed by middle and high school teachers. There were no differences in teachers’ beliefs about teaching and learning across school levels. Multigroup structural equation modeling indicated no differences in the influence of beliefs about teaching and learning on active learning teaching strategies among school levels, except that the traditional conception was negatively associated with the implementation of active learning in middle school. Constructivist beliefs were positively associated with active learning strategies across all levels, whereas teaching experience was negatively associated with teaching strategies that promoted student output at the higher school level. These findings have implications for the implementation of active learning, particularly at higher school levels.

When deep neural networks are deployed in a highly automated vehicle for environment perception tasks in an unseen (target) domain that differs from the training (source) domain, the mismatch will result in decreased performance. Domain adaptation methods aim at overcoming this mismatch. Many recently investigated methods for unsupervised domain adaptation train a model using labeled source data and unlabeled target data at the same time. These methods assume that data from the target domain is available during the source domain training, which is not always the case in real applications. In this paper we present a way to perform an online style transfer for continual domain adaptation which improves performance on (multiple) unseen target domains using a given perception model. The approach is based on an image style transfer in the frequency domain and requires neither an adjustment of the given source-trained model parameters to the target domain, nor does it require any considerable amount of memory for storing its frequency domain representation of the source domain style, which is particularly important considering the hardware limitations in an autonomous vehicle.

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.

The study focused on the effects of active learning strategies on the performance of Thai students in Mathematics at Plookpanya Learning School serves as a foundation of the intervention plan during the academic year 2024-2025. The primary respondents were the junior high school students enrolled at Plookpanya Learning School. However, this study was confined to a certain population type and area; data shall only be collected from students in Plookpanya Learning School in Thailand; this was a major limitation in terms of generalizing the study findings in other learning settings or other zones of the globe. The findings revealed that active learning strategies are consistently utilized by teachers in teaching mathematics to Thai students in terms of student engagement, use of collaborative learning techniques, implementation of technology, diversity of teaching methods, and feedback and reflection opportunities. Also, there is a high increase in the performance of Thai students after utilizing active learning strategies. Then, Thai students' performance in science classes is correlated with their use of brain-based learning strategies. Active learning strategies used by teachers in teaching mathematics, such as student engagement, use of collaborative learning techniques, implementation of technology, diversity of teaching methods, and feedback and reflection opportunities, are correlated to the performance of Thai students. Moreso, the use of active learning strategies in teaching mathematics significantly contributed to enhancing student engagement and performance among Thai students. As an outcome of the findings and the conclusions, the following recommendations were enumerated: Teachers may incorporate educational technology tools, such as interactive math software, online simulations, and educational games, to create an engaging learning environment that can provide instant feedback and adaptive learning experiences; Teachers may assign students videos or reading materials to study at home and use class time for hands-on activities and discussions; Teachers may encourage group work and collaborative projects where students solve mathematical problems together; Students may actively engage in the learning process by asking questions, seeking help when needed, and being open to feedback; School administrators may organize workshops and training sessions for teachers focused on active learning strategies and the effective use of technology in the mathematics classroom; and School administrators may encourage open communication between teachers, students, and parents to promote engagement and support for active learning initiatives.

Faculty motivations to use active learning among pharmacy educators