Application Of Active Learning Research Articles

Active methods in Entrepreneurship Education: a case study with engineering students

ABSTRACT Future engineers are labelled entrepreneurial engineers, possessing business skills that enable them to perform effectively in various contexts. Entrepreneurship education is a key avenue for equipping engineering students with these skills, with a growing propagation in their curricula in recent years. Still, scholarly efforts are limited in this domain, particularly regarding the application of active and experiential learning methods, despite early studies highlighting their effectiveness in developing entrepreneurial competencies. This case study presents a course for engineering undergraduates based on active learning theories and methods. We offer a comprehensive view of their perceived usefulness, opportunities, and challenges from the perspectives of the students, educator, and entrepreneurship education experts. This study advances the discussion on the intricacies of active learning applications in entrepreneurship, contributes to the limited literature on engineering entrepreneurship education, and provide insights to educators and pedagogical designers to effectively incorporate active methods into their curricula.

Instructor perceptions of active learning in higher education in Oman and students' commitment to the approach

PurposeCreating active learning opportunities requires building a learning culture in which the instructor plays the role of a facilitator, leaving the ultimate responsibility of learning to the student. The question, however, is whether this is happening in practice. This study aims to answer this question through instructors' perceptions of active learning in a higher education institution in Oman.Design/methodology/approachThe study participants were 85 instructors working for a private university in Oman. Data were collected by surveying these instructors' perceptions of active learning practice indicators, such as active teaching strategies and student educational practices. This was followed by interviewing a random sample of the same instructors (N = 10) to obtain a deeper understanding of their implementation of the active learning approach.FindingsData collected through the survey revealed that the shift from passive to active learning in higher education in Oman created a discrepancy between instructors' willingness to practice active learning and learners' unpreparedness to become autonomous learners. The follow-up interview findings confirmed this point, revealing instructors' negative perceptions of student participation and engagement in out-of-class activities.Originality/valueThis study is among the first to investigate the application of active learning in a higher education institution in Oman from the perspective of instructors.

Impact of using active learning principles on perception of medical students toward e-learning environment

Introduction: The COVID-19 pandemic has necessitated a transition from face-to-face to e-learning, which caught many medical educators unaware. However, it is imperative to develop skills as an e-educator through the application of active learning (AL) principles based on Bloom’s taxonomy. This study highlights the effect of using AL principles on the perception of students toward an e-learning environment using Dundee ready education environment measure (DREEM) questionnaire. The aims of this study were as follows: (1) to introduce the AL principles during online teaching learning sessions. (2) To assess the feedback on the module among the students. (3) To evaluate the change of perception of students toward the e-learning environment through the DREEM questionnaire. Materials and Methods: An educational interventional study was carried out among (76) undergraduate students of a medical institution in coastal Andhra Pradesh. Perception toward the e-learning environment was collected through the DREEM questionnaire online before and after the intervention (application of AL principles as per the Bloom’s taxonomy during online T/L sessions). Statistical analysis: The data thus collected were analyzed using IBM SPSS version 20 statistical software, descriptive and inferential statistics for quantitative data and thematic analysis for qualitative data. Results: The Global DREEM score was 122 which was 62 before the intervention. Similarly, 68% reported positive perception which was only 37% initially. Students’ social self-perception scored lowest initially, but scores were improved to 2nd highest after the intervention. For a few items such as teacher-centered, nonstimulating teaching, not being able to memorize, lack of motivating environment, feedback, feeling lonely, bored, and not having a good support system, the individual scores were negative, i.e., <2 which became positive, i.e., >3.5 after. Conclusion: There was a significant change perceived by the students toward the e-learning environment with increased DREEM scores. Thus, this study concludes use of AL principles in online platforms is effective in improving students’ perception of the e-learning environment. The following core competencies are addressed in this article: Practice-based learning and improvement, Medical knowledge, Interpersonal and communication skills.

The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds

The formation of aerosol particles in the atmosphere is driven by the gas to particle conversion of extremely low volatile organic compounds (ELVOC), organic compounds with a particularly low saturation vapor pressure (pSat). Identifying ELVOCs and their chemical structures is both experimentally and theoretically challenging: Measuring the very low pSat of ELVOCs is extremely difficult, and computing pSat for these often large molecules is computationally costly. Moreover, ELVOCs are underrepresented in available datasets of atmospheric organic species, which reduces the value of statistical models built on such data. We propose an active learning (AL) approach to efficiently identify ELVOCs in a data pool of atmospheric organic species with initially unknown pSat. We assess the performance of our AL approach by comparing it to traditional machine learning regression methods, as well as ELVOC classification based on molecular properties. AL proves to be a highly efficient method for ELVOC identification with limitations on the type of ELVOC it can identify. We also show that traditional machine learning or molecular property-based methods can be adequate tools depending on the available data and desired degree of efficiency.

Active Learning on Students 'Interpersonal Skills Impact: An Online Theater Approach

This study aims to examine the improvement of interpersonal skills through the application of active learning in the teaching and learning of art education. The approach used is the theatre-play approach. Theatre games are used as a fresh and appropriate approach to learning art education subjects through various types of games that are applied with various theatre techniques, including improvisation, role playing, physical exercise, and voice acting. The subject of art education is a theoretical and practical one that requires high focus during learning. Therefore, theatre games are applied to encourage active learning and prevent students from getting bored when learning is conducted online. Students play, act, improvise, and sing while learning. This qualitative study involved eight Form 5 students at the Malaysian Art School using observation and interview methods. The study applies social learning theory and constructivist learning theory. The data was thematically analysed using Nvivo software. Findings found that active learning takes place in learning through the application of a theatre game approach and highlights the interpersonal skills of students. Two-way communication occurs in every activity because the activity is carried out in groups. Studies on the learning of art subjects that apply this active learning through theatre games are believed to promote confidence and stimulate students to improve their interpersonal skills without realising it.

Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification.

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine’s process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Efektivitas Pembelajaran Aktif Mikir Pada Pelajaran Bahasa Indonesia Kelas V Mis Mutiara Sei Mencirim

The research that has been carried out is entitled "The Effectiveness of MIKiR Active Learning in Class V Indonesian Language Lessons at MIS Mutiara Sei Mencirim". This research aims to determine the implementation of MIKiR active learning in class V Indonesian language lessons at MIS Mutiara Sei Mencirim, the effectiveness of MIKiR active learning applied in class V Indonesian language lessons at MIS Mutiara Sei Mencirim, and factors that support and hinder the implementation of MIKiR active learning in lessons. Indonesian language class V MIS Mutiara Sei Mencirim. This research is a qualitative research with a case study type of research. The subjects of this study were teachers and fifth grade students. Data collection was taken through interviews, observation, documentation and questionnaires. The author takes the role of a direct interviewer to dig up more complete data for teachers and students. The results of this research show that the implementation of MIKiR active learning in Indonesian language lessons can be proven from the learning outcomes which consist of Experiencing, Interaction, Communication and Reflection. In the "Experiencing" activity the teacher involves the students' five senses to understand the concepts of the material presented so that it is easier to understand, "Interaction" the teacher encourages students to be able to express ideas and reflect on themselves so that it also supports good understanding of the concept, "Communication" the teacher motivates the participants students to be brave and fluent in conveying ideas, and teacher "Reflection" creates an attitude in students to be willing to accept criticism and improve themselves, both ideas, work results and attitudes. MIKiR's active learning is very effectively applied to Indonesian language lessons for class V MIS Mutiara Sei Mencirim, as evidenced by the fact that during the learning process students always feel excited and happy, initially students still feel shy in asking questions or expressing their opinions, but often MIKiR's active learning this is applied at MIS Mutiara Sei Mencirim they don't feel boredom, in this case active learning MIKiR is very effectively used in Indonesian language lessons for class V MIS Mutiara Sei Mencirim. supporting factors in the application of MIKiR active learning, namely the teacher's role in this learning activity is very important because teacher creativity determines the learning process using MIKiR active learning and students will become the main character or role in each lesson, the teacher only needs to direct students in the learning process and students will show their respective creativity, and the inhibiting factor in the application of active learning MIKiR is the allocation of learning time that is not in accordance with the material because during the discussion process students who do not understand will tend to ask a lot of questions in order to be able to complete their group assignments.

Discovery-Based Approach Combined with Active Learning to Improve Student Learning Experiences for STEM Students

A survey is administered to understand the application of active learning in undergraduate STEM courses. The findings observed over an academic year reflect current trends in the use of active learning. A combination of discovery-based learning and Pro-Con Grids is then used to improve long-term retention among STEM students. Students in a STEM course were provided with a course lecture prior to in-class sessions. A quiz was used for each lecture to gain insight into students’ understanding and to evaluate the effectiveness of the learning method. To measure the effectiveness of the proposed discovery-based learning two categories of metrics are used: learners' outcomes (grades) and survey (process measure) with 64 participants. The responses from the students show that discovery-based learning convinced students to find and use information rather than memorizing and repeating concepts and facilitate effective group work. In addition, the grade comparisons for three semesters indicate that the higher the use of discovery learning during the course the higher the course grades are found to be.

Active Learning and Professional Development: A Case of Thai Chinese Teachers

While Chinese has been taught as a school subject outside of China for decades, there is little research on how Chinese subjects are taught in secondary schools outside of China’s mainland with regard to the use of active learning and the impact of professional development events on teachers’ professional growth in classroom Chinese teaching. This article explored Thai Chinese teachers’ use of active learning methods at public and private secondary schools in the south of Thailand. It also examined the impact of a government-administered Chinese teaching seminar on their professional development. A sequential mixed-methods explanatory design was employed, involving focus group interviews and pre- and post-tests. The phenomenological approach with thematic analysis was used to analyze the qualitative data from the focus group interviews, while the Wilcoxon test was run to compare the pre- and post-test data. Findings revealed the application of active learning with discussion and role-playing activities involving audio-visual materials. The pedagogy of Thai Chinese secondary schools has shifted from knowledge transmission through lectures to knowledge generation and transformation via dynamic learning activities. After attending the seminar, teachers’ comprehension of active learning methods increased considerably (Z = 3.740, p < 0.001). However, teachers expressed concerns over the lack of innovative teaching techniques for teaching Chinese characters and the problems encountered by both students and teachers during the teaching and learning process. This study recommends that Hanban and the Thai Ministry of Education plan their seminars and workshops in a way that allows Thai Chinese instructors to continuously address their students’ and their own deficiencies while they are engaged in active learning activities, and it calls for additional research on the use of active CFL methods in secondary schools outside of China.

Flexibility of Virtual Team-based Learning in Teaching Medical Genetics

Team-based Learning (TBL) is a global education strategy designed to inculcate self-directed and active learning. The fallout is a new breed of students who are exposed to restricted and compromised teaching and learning methods via online platforms amidst this COVID -19 pandemic. Therefore, this innovation aims to explore the application of active learning among undergraduate medical students through virtual TBL. In this pilot study, the concept of medical genetics is introduced virtually through utilizing online platforms, Microsoft TeamsTM and Universiti Kebangsaan Malaysia (UKM) Learning Management System (UKMFolioTM), while maintaining the principles of TBL. Emphasis is placed on applied learning to apply the fundamental knowledge learnt during the preparation phase through team discussions and subsequent pitching presentations. Our study indicates no significant difference (p=0.404) in the mean score of students who participated in conventional face-to-face TBL sessions and virtual TBL. This suggests that active and self-directed learning can be integrated through virtual means with reproducible excellent results as with conventional face-to-face TBL. Nonetheless, issues related to Internet stability and insufficient content experts must be addressed if we are to tackle successful virtual teaching and learning.

Teaching of Magnetism Unit Topics Via Active Learning Applications

The aim of this study is to investigate the effect of active learning on students’ achievement in the unit of magnetism. It is important to create proper teaching environments in order to ensure the active participation of students in learning processes. Many different methods and techniques may be used for active participation. Detailed information should be provided for the teachers on how these methods and techniques can be implemented. For this reason, an active learning environment was created in the present study by way of using worksheets accompanied by experiments, simulations, modelling, garden activity and games. It was explained how these activities are implemented for each of the acquisitions related with the magnetism unit topics. The study employed sequential explanatory mixed design. The subject of this study consisted of 98 eleventh grade students in a high school in Türkiye. The data were acquired via an open ended and multiple-choice conceptual achievement test, observation forms and interviews. The findings obtained illustrated that active learning applications increase academic achievements of students at a statistically significant level. In addition, it was concluded that active learning applications make a positive impact on the interest, attitude and motivations of the students towards the physics course. The results show that magnetism unit topics are effective in providing a conceptual change. It is suggested to use the applications shared in the present study for the teaching of abstract subjects such as magnetism. In this way, it may be possible to create learning environments that will enable instructors to reach the desired goals.

On the application of active learning for efficient and effective IoT botnet detection

The active learning approach for machine learning can greatly benefit those environments where a wealth of unlabeled data is available, and the labeling cost of the data can be restrictive. In this regard, security operations centers (SOCs) can take advantage of the human expertise available to improve machine learning-based detection models using the active learning approach. In the context of SOC operations and IoT botnet detection, our study provides a thorough benchmarking of the application of different active learning approaches within the framework of pool-based sampling. The selection of the optimal query instance for learning is evaluated using uncertainty sampling, ranked batch-mode sampling, and query by committee strategies. Our results show that the active learning approach can help to generate better detection models using all the active learning query strategies tested in our benchmarking setup. Leveraging the human–machine interaction can produce high-performance models in the context of IoT botnet detection using significantly less data than the passive approaches traditionally used for the generation of machine learning-based detection systems. Additionally, the impact of wrong-labeled data in the active learning implementation is explored.

IMPLEMENTASI PEMBELAJARAN AKTIF DALAM HABITUASI HARI BERBAHASA ARAB JURUSAN KEAGAMAAN DI MA DARUL ULUM PURWOGONDO KALINYAMATAN JEPARA

The application of active learning in learning Arabic at MA Darul Ulum Purwogondo Kalinyamatan Jepara. The implementation of active learning is learning that is made so that students can take part in learning actively and fun. So that students have many roles in participating when the learning process takes place in order to improve the quality and satisfactory results. This study aims to determine the level of interest of students in Arabic subjects at MA Darul Ulum Purwogondo Kalinyamatan Jepara. This research is a qualitative research. Data collection techniques using the method of observation, interviews, and documentation. Research on the implementation of active learning in Arabic subjects majoring in religion at MA Darul Ulum Purwogondo Kalinyamatan Jepara has been going well by emphasizing Arabic-language students majoring in religion with the concept of an Arabic day every Sunday. In the process of implementing active learning for Arabic subjects, the teacher provided a stimulus or stimulus that aims to be motivated and understand what the learning objectives are to be studied. Active learning is carried out after the educator delivers the material by asking questions to students individually or in groups (if forming groups). Educators also give awards or rewards to groups that can answer questions from educators based on teams that work together correctly and well.

Large deviations of semisupervised learning in the stochastic block model.

In semisupervised community detection, the membership of a set of revealed nodes is known in addition to the graph structure and can be leveraged to achieve better inference accuracies. While previous works investigated the case where the revealed nodes are selected at random, this paper focuses on correlated subsets leading to atypically high accuracies. In the framework of the dense stochastic block model, we employ statistical physics methods to derive a large deviation analysis of the number of these rare subsets, as characterized by their free energy. We find theoretical evidence of a nonmonotonic relationship between reconstruction accuracy and the free energy associated to the posterior measure of the inference problem. We further discuss possible implications for active learning applications in community detection.

On the Application of Active Learning for Efficient and Effective Iot Botnet Detection

On the Application of Active Learning for Efficient and Effective Iot Botnet Detection

The Reality of Active Learning Application in Jeddah Schools by English Teachers

This study explored the extent to which teachers of English as a Foreign Language (EFL) adopt and implement active learning strategies in their classrooms. The study, also, examined the obstacles encountering high school teachers to use active learning and it delved further into exploring the participants’ recommendations to mitigate these obstacles. The study followed a quantitative methodology. Sixty-six EFL teachers (Male n=22 and Female n=44) from Jeddah in the Kingdom of Saudi Arabia participated in this study. The researchers used an electronic custom-designed, 19-items rated on a five-point Likert scale questionnaire, for ease of dissemination and data collection. The findings revealed that the degree of employing active learning was medium (54.8%). However, 55% of the participants responded that they encountered some acute obstacles to implementing active learning in their classrooms. The findings showed no statistically significant differences attributed to gender, experience, and training about using active learning, obstacles, and recommendations to overcome the obstacles. The study discusses some of the obstacles to implementing active learning and concludes with some recommendations to the Ministry of Education to reinforce active learning in the education system of EFL contexts.

Reclaiming agency: skills, academics and students in the Social Sciences

The adoption of active learning pedagogy and, later, the institution of the employability agenda in Higher Education have resulted in a severe loss of agency for academics and students in the Social Sciences. In this article, we reflect on our experiences of applying active learning methods. We argue that we have been part of a change that has occasioned a loss of key skills development, especially those associated with traditional learning and academic thinking. An overly headlong rush to implement the “new” over the “old” saw the discarding of certain skills central to the active learning agenda. Further, the emphasis on student satisfaction, professionalisation and quality assurance pushed the academic to the sidelines, to the detriment of Higher Education. We, therefore, first critique the skills debate and identify shortcomings in the active learning application that emerged from that debate. We focus on the skills emphasised in practice, how they are portrayed in opposition (instead of complementarity) to academic skills, and how they undermine the agency academics and students really require. Next, we propose a reconsideration of necessary but undervalued skills like reading, listening and note-taking.

Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network-Corrected Labeling in Magnetic Resonance Imaging-Derived Proton Density Fat Fraction.

This study aimed to propose an efficient method for self-automated segmentation of the liver using magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) through deep active learning. We developed an active learning framework for liver segmentation using labeled and unlabeled data in MRI-PDFF. A total of 77 liver samples on MRI-PDFF were obtained from patients with nonalcoholic fatty liver disease. For the training, tuning, and testing of the liver segmentation, the ground truth of 71 (internal) and 6 (external) MRI-PDFF scans for training and testing were verified by an expert reviewer. For 100 randomly selected slices, manual and deep learning (DL) segmentations for visual assessments were classified, ranging from very accurate to mostly accurate. The dice similarity coefficients for each step were 0.69 ± 0.21, 0.85 ± 0.12, and 0.94 ± 0.01, respectively (p-value = 0.1389 between the first step and the second step or p-value = 0.0144 between the first step and the third step for paired t-test), indicating that active learning provides superior performance compared with non-active learning. The biases in the Bland-Altman plots for each step were - 24.22% (from - 82.76 to - 2.70), - 21.29% (from - 59.52 to 3.06), and - 0.67% (from - 10.43 to 4.06). Additionally, there was a fivefold reduction in the required annotation time after the application of active learning (2 min with, and 13 min without, active learning in the first step). The number of very accurate slices for DL (46 slices) was greater than that for manual segmentations (6 slices). Deep active learning enables efficient learning for liver segmentation on a limited MRI-PDFF.

Penerapan Active Learning dan Penggunaan Media Berbasis Komputer untuk Meningkatkan Prestasi Belajar Siswa pada Pembelajaran IPA

This study aims to analyze the effectiveness of the application of active learning in increasing interest, attention, and activity, as well as students' learning achievements in science lessons. The subject of the study was a grade VI student of MI Negeri 1 Yogyakarta. This study uses methods of action through the process of incugate. The results showed that the application of active learning through inkuiri method can train students to discover for themselves various concepts learned, foster the ability to think, work, and be scientific and can improve and expand the mastery of skills in the process of acquiring cognitive students.