An Active Learning Approach to Diversity Training

Active learning approaches require a lot of time investment in student activities and engagement during the class period, which often leads to incomplete coverage of the course syllabus. Furthermore, it requires significant amount of time for the instructor to design and implement active learning strategies. All these shortcomings are often cited as the common hindrance in adopting student active learning. To address these widely recognized inhibiting factors, we recently implemented a new student active learning approach. This approach is based on the students’ presentation and hence termed as student presentation-based active learning (SPAL). Under this approach, students are given a reading assignment to prepare a PowerPoint presentation on well-defined conceptual topics, questions, or chapter modules. Reading assignments on a topic are administered 1–2 weeks before covering them in the class. This allows reasonable time for the self-comprehension of the suggested material for presentation preparation. Students were expected to rehearse the presentation and be prepared to complete it in the suggested time duration. During each lecture, one group of student would present the assigned topic to the class, and their presentations were graded according to the rubric focusing on the coverage of suggested topics, quality of presentation, and after presentation discussions. Peers and instructor provided feedback about the students’ presentation and unclear concepts. To understand the efficacy of this approach, this approach was implemented in Mechanical Engineering Senior Capstone Project I course in Fall 2016. Here the responses and insights garnered from this practice were presented, and discussion on the advantages and challenges associated with the adaptation of this approach in teaching engineering courses as compared to lecture based classroom education system. This paper can provide useful insights for instructors considering this approach or similar student active teaching approach in their courses.

Recent developments in the field of Information Technology call for a specific reconsideration of the traditional educational methods and facilitate development of new teaching and learning methods and tools, opening it for a wider scope of concerned persons. Active teaching and learning methods as part of educational methodology are widely used at the universities across the European community, putting the larger responsibility of learning at the hands of the learners themselves. One of the major challenges is effective addressing the learning needs of the new generation of students. Postgraduate education in in the field of logistics and supply chain management (LSCM) must keep it in mind in order to promote a suitable answer to the LSCM labor market needs. This is accomplished well by a master programme in LSCM with a European dimension emerged from the synergy of international cooperation between Academia and the professional bodies. This paper shares the empirical study on implementation of Master study programme of Logistics and Supply Chain Management in the Riga Technical University and extensive use of active and innovative learning approach.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

Active Learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques.

Public health programs are increasingly offering health policy content to meet workforce demand for public health professionals with health policy analysis and communication skills. Undergraduate public health education in health policy and related curriculum is lacking. Existing work describes service- and experiential learning approaches, which may not be feasible in large courses or public health programs with limited administrative or financial resources. This article describes health policy curriculum, learning objectives, and course activities (i.e., individual writing assignment, mock policy debate, and policy analysis exercise) that integrate an active learning approach and use simulation to develop policy analysis and communication skills in an undergraduate classroom setting. An active learning approach can increase teaching effectiveness, critical thinking, peer interaction, and student engagement to cultivate these applied skills. The described curriculum has been used in various modalities (in-person, hybrid, and remote) with positive reception in a public research university with a high percentage of first-generation college students. Student course evaluations and an independent assessment of the policy analysis exercise reflect high student interest, engagement, problem solving, satisfaction, perceived benefits, and application of policy analysis and communication skills, providing support for teaching health policy curriculum to undergraduate students using an active learning approach. The discussion includes strategies to address resistance by public health instructors and students to using active learning techniques.

We compare the level of accuracy achieved by active learning and domain adaptation approaches for quality of transmission estimation of an unestablished lightpath, in presence of small-sized training datasets.

In India, traditional learning approaches in universities follow passive learning and instruction‐based studies. The demand for evidence‐based instructional and interactive active learning processes increases with the advancement in technology. To improve the quality of teaching and student performance, Laureate International University network has taken a unique initiative by applying an iterative and evidence‐based active learning process in small groups at the University of Petroleum and Energy Studies, Dehradun for the Postgraduate Certificate in Academic Practice program. In this study, active learning processes with digital devices (mobile devices) and digital technologies (modeling tools, simulation, and online resources), having virtual small‐to‐medium strength classrooms are integrated into various scenarios with different levels of education. This has been found to be useful in improving student performance during the COVID‐19 pandemic. The proposed process applies both traditional and active learning processes with the provision to use mobile devices for digital content. This process also involves teachers attending a Bootcamp, over a 2‐month period, consisting of four modules in which they learn about and use digital content that will then be applied in their courses. Results show that the active learning process is of great benefit to students over traditional learning, and it provides a 30% improvement in student's grades. Further, it is observed that long‐term learning average marks increase by 66.9% in two engineering subjects. The simulation‐based experimentations are conducted to engage students and faculty members in active learning and simulation learning processes. It shows that the proposed approach (active learning) improves students' learning abilities as compared to the traditional approach.

Staying Active

Active learning is one form of supervised machine learning. In supervised learning, a set of labeled samples is passed to a learning algorithm for training a classifier. However, labeling large amounts of training samples can be costly and error-prone. Active learning deals with the development of algorithms that interactively select a subset of the available unlabeled samples for labeling, and aims at minimizing the labeling effort while maintaining classification performance. The key challenge for the development of so-called active learning strategies is the balance between exploitation and exploration: On the one hand, the estimated decision boundary needs to be refined in feature space regions where it has already been established, while, on the other hand, the feature space needs to be scanned carefully for unexpected class distributions. In this thesis, two approaches to active learning are presented that consider these two aspects in a novel way. In order to lay the foundations for the first one, it is proposed to express the uncertainty in class prediction of a classifier at a test point in terms of a second-order distribution. The mean of this distribution corresponds to the common estimate of the posterior class probabilities and thus is related to the distance of the test point to the decision boundary, whereas the spread of the distribution indicates the degree of exploration in the corresponding region of feature space. This allows for the evaluation of the utility of labeling a yet unlabeled point with respect to classifier improvement in a principled way and leads to a completely novel approach to active learning. The proposed strategy is then implemented and evaluated based on kernel density classification. The generic active learning strategy can be combined with any other classifier, but it performs best if the derived second-order distributions are sufficiently good approximations to the sampling distribution. Although second-order distributions for random forests are derived in this thesis, they do not approximate sufficiently well the sampling distribution and mainly allow only for the relative comparison of prediction uncertainty between test points. In order to combine the state of the art classification performance of random forests with the principal ideas of the first active learning approach, a related second approach for random forests is derived. It is, in addition, tailored to the demands in industrial optical inspection: bag-wise labeling with weak labels and strongly imbalanced classes. Moreover, an outlier detection scheme based on random forests is derived that is used by the proposed active learning algorithm. Finally, a new computational scheme for Gaussian process classification is presented. It is compared to two standard methods in geostatistics, both with respect to theoretical consistency and practical performance. The method evolved as a by-product when considering using Gaussian process models for active learning.

In health education, active learning approaches have gained increasing importance for enhancing the quality of learning, clinical reasoning processes, and socio-emotional skills. The “Cogniverse” is a novel, interactive, and low-cost pedagogical tool that promotes multimodal learning through verbal, associative, and nonverbal communication. This study describes and analyzes the implementation of the “Cogniverse” in health education as a strategy to improve active learning, teamwork, and integrative cognitive engagement. Grounded in social constructivism and Bloom’s Revised Taxonomy, the method focuses on developing higher-order cognitive and socio-emotional competencies through three consecutive stages—verbal explanation, one-word clue, and nonverbal representation—each targeting distinct dimensions of cognitive representation and meaning. The organized use of this activity fosters semantic activation, concept formation, embodied cognition, and collaborative learning, ultimately enhancing memory retention. The findings indicate that engaging in active, play-based learning stimulates motivation, empathy, and clinical reasoning among students and faculty members. Overall, “The Cogniverse” represents a successful, user-friendly, and humanized active learning technique that promotes interdisciplinary learning, communication skill development, and professional competencies aligned with patient-centered care.

Real-time safety assessment of the complex dynamic systems in nonstationary environments is of great significance for avoiding the potential hazards. In this case, the update procedure with high assessment accuracy and training speed is crucial and meaningful in the dynamic streaming setting. Generally, the performance of most online learning approaches will be negatively affected by limited annotated samples in such a setting. Moreover, the time cost of advanced conventional methods with retaining procedures is relatively high, constraining their practicality. In this article, a novel online active broad learning approach, termed OABL, is proposed. In detail, the effectiveness of the broad learning system in the framework of online active learning is first revealed and verified. A reasonable dynamic asymmetric query strategy is then designed with a limited annotation budget to actively annotate the relatively valuable samples, which is beneficial to mitigating the negative effects of class imbalance. In this context, the advantage of the human-in-the-loop characteristic is also effectively used to control the evolution direction of the learner during the incremental update, which makes it better able to adapt to complex and nonstationary environments. Several related experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. Results show the effectiveness and practicality of the proposal compared with the existing advanced approaches.

Dynamic reliability analysis of Aerial Building Machine under extreme wind loads using improved QBDC-based active learning

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines (SVMs), the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric SVM classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using four case studies: 1) dc/dc converter ripple noise analysis; 2) phase-locked loop lock-time verification; 3) reliability analysis of a ring oscillator with respect to process variations and initial conditions; and 4) prediction of chip peak temperature using a limited number of on-chip temperature sensors.

Project development is a vital component contributing to the continuous assessment marks for some courses in Institutions of Higher Learning. This study was conducted to develop video-based modules to reference students during the development of the database project used as active learning in the classroom. Video-based modules are suitable for learning approaches toward students in Gen-Z. ADDIE model has been used for the video-based module development. The video module development consists of several phases that have been set according to the topic of database project development. This study involved 104 students in year two who will need to develop the database project individually. The results of the study found that the video-based module had a positive impact on the students. The descriptive analysis has shown that 101 (97.1%) students agreed that the video-based module produced is easier to understand. Meanwhile, 100 (96.2%) students found that this video based module helps them complete the given project. This active learning approach can be used for other courses that involve project development and having a class with a large number of Gen-Z students.

Active learning can serve as a platform for addressing sustainability, ethics, and social entrepreneurship by engaging students in experiential activities with external organisations for initiatives with a social mission. A variety of active learning approaches provide an opportunity to promote collaboration between students and external organisations in pursuit of a socially beneficial goal. This paper highlights different active learning approaches that emphasise sustainability, ethics, and social entrepreneurship in undergraduate and graduate management education. These approaches include experiential learning methods, such as undergraduate case competitions and global MBA case studies, as well as action learning methods, such as live consulting projects and cross–national student collaborations. These active learning approaches can help students address ethical, social, and environmental issues in complex business situations.