Active Learning Strategies Research Articles

Effects of computer-assisted learning for removable partial denture design on learning outcomes and satisfaction

BackgroundComputer-assisted learning (CAL) has the potential to enhance learning outcomes and satisfaction. However, there are limited reports in the literature that describe or evaluate the implementation of this method to promote competency-based learning in removable partial denture (RPD) design. Therefore, this study aimed to: (1) compare the effectiveness of different learning methods using a 3D software-aided RPD design program, (2) evaluate the learning outcomes associated with these different methods following active learning, and (3) assess students’ satisfaction.MethodsForty undergraduate dentistry students were divided into four groups of ten students. Each group was assigned different learning methods for a period of 28 days using the CMU-3D RPD Design program. The learning methods included lecture-based learning (LBL), case-based learning (CBL), a combination of LBL and CBL, and a control group that did not engage in LBL or CBL. Following this period, participants in all four groups engaged in active learning activities related to RPD design. Learning outcomes were measured three times: prior to the experiment, after using the software, and after participating in active learning activities.ResultsAnalysis of pre- and post-training assessments using multiple-choice questions revealed that dental students in the combined LBL and CBL group demonstrated statistically significant improvements in test scores compared to baseline (p < 0.05). Notably, there were marked enhancements in comprehension, application, and analysis skills. Outcomes following active learning were significantly improved in groups with a foundation in lecture-based learning. Furthermore, all groups reported high levels of satisfaction (over 90%) after utilizing the software and participating in active learning activities.ConclusionsComputer-assisted learning, combined with active learning strategies, improves learners’ outcomes and satisfaction. Learning RPD design through computer-assisted methods positively influenced learning outcomes, particularly when both lecture-based and case-based learning approaches were used. Active learning strategies resulted in more substantial improvements when applied to students with a strong foundation in lecture-based learning.

Preparing for a Nursing Interview Through Active Learning Strategies.

Preparing for a Nursing Interview Through Active Learning Strategies.

Art in Anatomy Session as a Method of Formative Feedback in Pre-Clerkship Medical Education.

Medical schools were incorporating active learning strategies in anatomy teaching to accommodate diverse student bodies. Formative assessment and art as a hands-on learning method had been explored as alternatives to traditional teaching methods. Those methods allowed students to practice and assess their understanding of anatomy as they progress. The current study investigated the effectiveness of "Art in Anatomy" lab sessions in enhancing preclerkship medical students' comprehension of challenging anatomical topics and determining if differences were related to their year in medical school. This study involved 41 pre-clinical year medical students at CUSM-SOM who participated in "Art in Anatomy" sessions. Results showed pre-session and post-session quiz scores, with differences calculated for first-year and second-year medical students. The study revealed a significant skew in pre- and post-session data, with Year 2 students showing lower mean and smaller range on pre-session quiz scores. Post-session quiz scores showed higher mean and median scores but reversed on post-session. Both Year 1 and Year 2 students showed improved scores, with 68% experiencing a score increase of 0, 1, 3, or 4 points, and 32% experiencing a 2-point increase. Art in Anatomy sessions could effectively support medical students in learning human anatomy during pre-clerkship years. The method provided formative feedback, aiding immediate recall of anatomical knowledge. Future research should explore different art forms and correlate post-session quiz scores with other students' exam scores, such as End of Course, NBME, and practical exam scores.

Exploring the effectiveness of AI-generated learning materials in facilitating active learning strategies and knowledge retention in higher education

Purpose This study aims to explore the intersection of AI-generated learning materials and active learning strategies in higher education artificial intelligence (AI) is bringing about changes and opening up new possibilities for an improved and more efficient higher education. However, the argument is that its use in education/classroom should be informed by verifiable evidence as well as best practice, which this scholarly work helps build evidence-based research to assess this technology in higher education. Design/methodology/approach Primary data was collected through structured questionnaire administered online via Google form. Based on the non-probability sampling technique, 300 higher education tutors and students across the UK were purposively targeted, out of which 218 (72.7%) response rate was achieved. Data was analyzed using descriptive statistics with the aid of Statistical Package for Social Sciences, whereby regression, correlation and Chi-square tests were conducted to determine the statistical significance, direction and strength of the relationship between the measured variables. Findings This study revealed that AI-generated learning materials support active learning strategies that enable students to actively engage in their learning, likewise enabling students to develop deeper understanding of their course content with significantly better knowledge retention, which is critical to the learning process. However, findings further revealed that acceptance/regular use of AI-generated learning materials is still below par across the higher education institutions, and there is major concern that the benefits may not be fully realized due to barriers to adoption. Research limitations/implications There are limitations that future studies can improve on, especially in terms of methodology. Pragmatism is a philosophical research stance that integrates quantitative data collection with qualitative data (such as interviews) and will ask in-depth questions to gain holistic quality data for such empirical. Future studies can also improve on the research scope to allow for generalizability of findings and check for potential biases in the data collection, analysis and interpretation processes. Originality/value Despite the huge anticipation regarding how AI technology could transform teachers’ roles in higher education, concrete research into AI-generated learning materials and actual impact in facilitating active learning strategies and knowledge retention is currently lacking. This study presents theoretical models on AI acceptance in higher education and explored the Technology, Pedagogical and Content Knowledge framework to inform empirical information on how AI can support active learning strategies and students’ knowledge retention.

Research-Based Instructional Strategies, Professional Competence, and Pedagogical Approaches

This study examines the effectiveness of research-based instructional strategies, professional competence, and pedagogical approaches among elementary teachers in the Schools Division of Iloilo, Philippines, during the 2023-2024 school year. The research employs a descriptive-correlational design to explore the relationships between these variables, with a focus on how teachers’ demographic profiles—such as age, sex, educational attainment, length of service, and position—influence their instructional methods and professional effectiveness. A total of 391 elementary teachers participated in the study, representing a diverse cross-section of the teaching workforce in Iloilo. The study utilized a validated and reliable questionnaire to gather data, which was then analyzed using various statistical tools, including frequency counts, percentages, means, T-tests, ANOVA, and Pearson correlation. The findings reveal that while there is a general uniformity in pedagogical approaches across different age groups, sexes, and levels of teaching experience, significant differences emerge when these approaches are classified by educational attainment and professional position. Younger teachers and those with less experience tend to prioritize active learning strategies and inquiry-based learning, while more experienced teachers and those in higher positions emphasize collaboration, continuous improvement, and differentiation in their teaching methods. The study also identifies a positive correlation between the use of research-based instructional strategies and professional competence, suggesting that teachers who engage with research tend to be more effective in their roles. However, the findings indicate that tailored professional development programs are necessary to address the specific needs and strengths of different teacher groups, particularly concerning their educational background and professional roles. Overall, this research contributes to the ongoing efforts to enhance the quality of education in the Philippines by providing insights into how teachers’ instructional strategies and professional competencies can be improved to better meet the diverse needs of their students. The study’s recommendations focus on fostering collaboration, promoting continuous professional development, and ensuring that teaching practices remain relevant and effective in an ever-evolving educational landscape. Keywords: research-based instructional strategies, professional competence, pedagogical approaches, elementary education, teacher development, educational attainment, teaching experience, classroom practices, student engagement, Philippines education system

An Inquiry-Based Learning Activity to Address Mental Illness Stigma

Background Mental illness stigma includes inaccurate stereotypes and is associated with treatment reluctance. Undergraduate psychology courses represent an opportunity for stigma reduction efforts, and yet mental illness stigma may be only minimally affected by standard offerings. Objective We present preliminary evidence of a single-day inquiry-based learning (IBL) activity to address mental illness stigma. Method Undergraduate psychology students ( N = 79) completed a stigma scale on the first day of class, participated in an IBL lab, and then completed the scale a second time. Results The mean score of mental illness stigma at the start of the semester was significantly higher than the postintervention mean score, providing preliminary evidence for the effectiveness of the activity. Teaching Implications This single-session IBL activity can be implemented in introductory to clinical psychology courses. Conclusion The activity shows promise as an active-learning strategy to address mental illness stigma. Future research is needed to build on the efficacy of the approach.

Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.

Building a supportive clinical learning environment: Orienting newly licensed nurses to their impact on the professional development of physician trainees

What was the educational challenge? Nurses play an essential role in the professional development of physician trainees within the clinical learning environment (CLE), but rarely receive formal training regarding this role. What was the solution? Utilizing a multifaceted, systematic approach, we developed an educational program for newly licensed nurses which addressed their role in the CLE and the professional development of physician trainees. How was the solution implemented? We delivered two 90-minute workshops to approximately 40 nurses during the 2021–2022 academic year. Participants completed workshop session evaluations and the Clinical Learning Environment Quick Survey (CLEQS). Data were descriptively analyzed. Workshops were positively received, with most participants rating them as very good/excellent (Workshop #1: 83.3% and Workshop #2: 72.2%). The CLEQS results suggested that the participants’ CLEs were predominantly healthy and supportive, with most respondents indicating that they would recommend their unit to colleagues (before Workshop #1: 92.2% and after Workshop #2: 100.0%). What lessons were learned that are relevant to a wider global audience? Our educational program acknowledges the important role nurses play in the professional development of physician trainees, and equips them with tools to promote teamwork, communication, and a growth mindset towards interactions with physician trainees. What are the next steps? We continue to iterate our interactive workshops to prepare nurses for their important role in the professional development of physician trainees. To employ more active learning strategies, we developed pre-workshop videos. Thus far, we have delivered these workshops to nearly 600 nurses.

CFD modelling and simulations of atomization-based processes for production of drug particles: A review.

Atomization-based techniques are widely used in pharmaceutical industry for production of fine drug particles due to their versatility and adaptability. Key performance measure of such techniques is their ability to provide control over critical quality attributes (CQAs) of produced drug particles. CQAs of drug particles produced via atomization critically depend on fluid dynamics of sprays; resulting mixing, heat and mass transfer; distribution of supersaturation and subsequent nucleation and growth of particles. It is essential to develop and use computational fluid dynamics (CFD) models for adequate understanding of multi-scale transport processes ranging from molecular scale mixing and particle scale processes, and from atomizer nozzle to overall spray chamber scale establishing relationships between CQAs and design and operating parameters of spray nozzle and chamber. In this work, we critically review past and current research efforts on CFD modelling of pharmaceutical atomization-based processes with an objective to provide clear assessment of the state of the art and to provide recommendations. An overview of the key atomization-based methods for producing drug particles with desired CQAs is presented. Key underlying physical processes and relevant concepts are then outlined. This discussion is related to the demands on CFD models; and state of the art is then discussed with respect to the process needs. Recommendations are provided towards higher fidelity and more efficient models of atomized multiphase flow dynamics and turbulence, drying modelling for the produced particles, and validation approaches. We conclude by highlighting a perceived need for numerical atomization studies with a pharmaceutical context; then, we deliver an outlook on current promising active control and machine learning strategies to augment the shift towards quality-by-design approaches in pharmaceutical manufacturing.

English Language Needs Assessment for Economics Students: An ESP Analysis

This research aims to identify and analyze the English language learning needs of non-English major students, specifically those studying economics. Utilizing a questionnaire distributed among students, the findings reveal that a significant majority (80%) perceive their English proficiency as beginner level, highlighting an urgent need for foundational language skills relevant to their field of study. The results indicate a strong preference for practical learning materials, particularly those related to economics, such as reading economic materials (70.6%), speaking skills with clients (76.5%), and mastering vocabulary related to economics (65.7%). Furthermore, the analysis highlights a gap between the current knowledge and the proficiency required for academic and professional contexts. Students expressed a desire to engage in learning activities that promote interaction and collaboration, emphasizing the importance of developing a curriculum tailored to their specific needs. This study underscores the necessity of implementing a needs-based curriculum that incorporates active learning strategies, thereby enhancing students' English language skills and preparing them for future workplace challenges in the field of economics.

Effectiveness of the Flipped Classroom Learning Model in Improving Students' Mathematical Reasoning Abilities in the Application of School Mathematics Course

This research aims to measure the effectiveness of the flipped classroom learning model in improving students' mathematical reasoning abilities in the Application of School Mathematics course at the Universitas Terbuka. The research method used was quasi-experimental with a pretest-posttest control group design. The research sample consisted of 60 students, who were divided into two groups: an experimental group that used the flipped classroom (n=30) and a control group that used the conventional method (n=30). The results of data analysis show that the average score of students' mathematical reasoning ability in the experimental group increased from 58.2 (SD = 8.7) in the pretest to 81.4 (SD = 7.2) in the posttest, with an average increase (N -gain) of 0.64 which is included in the medium category. In contrast, the control group experienced an increase from 57.6 (SD = 9.1) to 70.3 (SD = 8.5), with an N-gain of 0.38 which is in the low category. The t test showed that the increase in mathematical reasoning ability in the experimental group was significantly higher than in the control group (t = 4.12, p &lt; 0.001). These results indicate that the flipped classroom is an effective learning model in improving students' mathematical reasoning abilities. These findings provide an important contribution to the development of distance learning, especially at the Universitas Terbuka. This research recommends implementing the flipped classroom integrated with active learning strategies for other courses in mathematics education programs.

Mechanical property prediction of random copolymers using uncertainty-based active learning

The copolymer, a widely used material in our daily lives, presents a significant challenge in targeted sequence design. While recent advancements in computational simulation and data science offer a promising avenue for addressing this complex issue, challenges persist in labeled data scarcity. In this study, we introduce an uncertainty-based active learning framework for predicting the properties of random copolymers. We found that the active learning strategy allowed for labeling only 40 data points within the design space of 1550 data points, drastically reducing the labeling efforts by 97%. Most data selected by active learning were positioned on the design space’s periphery, transforming the learning task into an interpolation problem. Through integrating active learning and molecular dynamics, we successfully overcame the combinatorial explosion problem in copolymer sequence design, streamlining the data labeling process and culminating in a highly accurate model. This research demonstrates data science’s potential in polymer design, especially when facing data scarcity.

Exploring the Root Causes of Physics Aversion among Grade 12 STEM Students: A Qualitative Case Study at RPMD National Science High School

Physics aversion is a pervasive challenge in STEM education worldwide, significantly impacting students’ engagement and performance and highlighting the need to understand its root causes to develop effective interventions. This qualitative case study explored the root causes of physics aversion among Grade 12 STEM students at RPMD National Science High School in Marawi City, Philippines. Ten Grade 12 students enrolled in the 2023-2024 academic year were selected through purposive sampling. Data were collected through one-on-one in-depth interviews to capture participants’ perceptions and experiences with physics. The audio recordings were transcribed and analyzed using thematic analysis. From the data, 15 codes emerged, which were grouped into four overarching themes: Mathematical Challenges, Teaching-Related Issues, Language and Comprehension, and Personal and Social Factors. Key findings revealed that mathematical challenges, such as difficulty with complex equations, problem-solving, and formula memorization, were the most frequently cited barriers. These issues were often exacerbated by mathematics anxiety and inadequate foundational skills. Teaching-related issues, including ineffective instructional methods, disorganized presentations, and lack of engagement, significantly hindered students’ understanding and interest. Language barriers, especially in decoding technical terminology and understanding word problems, compounded these difficulties. Personal and social factors, including a lack of interest in physics, peer influence, and perceived irrelevance to future career goals, further contributed to the aversion. The study highlights the urgent need for student-centered teaching approaches, such as active learning strategies, differentiated instruction, and linguistic scaffolding, to address these challenges. Policymakers and educators are encouraged to implement holistic interventions to create a more inclusive and supportive learning environment. Future research should investigate these dynamics across diverse educational contexts and assess the impact of targeted interventions on reducing physics aversion.

Inquiry-Based Learning in Phonetics and Phonology: Promotion of Critical Thinking Skills in an EFL Higher Education Context

This study investigates the effectiveness of Inquiry-Based Learning (IBL) strategies in enhancing Critical Thinking (CT) skills and academic performance in the study of English Phonetics and Phonology among students at a Spanish Higher Education Institution. A total of 54 students from a Bachelor’s Degree in Primary Education were divided into control and experimental groups to compare traditional teaching methods with IBL methodologies. Results indicated that students in the IBL group showed significant improvements in academic performance, critical thinking skills, and long-term retention of complex subject matter compared to those in the control group. Furthermore, the study highlights that the IBL approach not only facilitates the immediate educational outcomes but also equips students with essential cognitive tools for real-world problem-solving. This research underscores the pedagogical value of learner-centered models in the study of English Phonetics and Phonology, promoting active engagement and systematic thinking. The findings contribute to the growing body of literature advocating for the integration of active learning strategies across various disciplines and educational contexts.

Exploring Active Learning Strategies in Science among Senior High School STEM Learners and Teachers

With the recent data revealing the performance status of Filipino learners in science aggravated by the negative effects of the pandemic, a concerted effort is directed toward exploring how learners learn science concepts in the current educational landscape to address issues of poor conceptual understanding and a learning gap. This qualitative study explored the perceptions of STEM learners on different ways of learning science concepts and how teachers developed the habits and attitudes of learning science among their students to develop conceptual understanding. Using a descriptive phenomenological approach, a semi-structured interview guide was conducted among 15 teachers with their corresponding students (n = 60) to explore their strategies and purposeful ways of learning and understanding concepts in science. Thematic analysis revealed five themes that describe STEM learners’ science learning process: extended reading, recalling, and rewriting; seeking help from MKO (more knowledgeable others); self-directed learning; acquisition from learning resources; and experiential learning. Both learners’ and teachers’ narratives emphasized the need for diverse approaches catering to the needs, interests, and contexts of every individual to promote the science of learning among students. While learners are struggling to concretize abstract concepts in science, their varied intentional science of learning enables them to navigate in the learning process.

Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data

This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection. In active learning, the minimum confidence scoring method and a sampling technique based on a data pool are employed to reduce labeling costs. The deep learning model incorporates a semantic segmentation gated full fusion module that integrates a dual attention mechanism. This module enhances the capture of detailed texture information, optimally allocates spectral weights, and improves the model’s ability to distinguish between similar categories. At a labeling cost of 20%, the average accuracy of the model is 93.2%. Compared with other models, the proposed model achieved the highest classification accuracy in the case of limited training samples. At full annotation cost, the average accuracy is 95.32%, with only a difference of about 2%, but saving 80% of annotation cost. Therefore, active learning strategies can filter out high-value samples that are beneficial for model training, greatly reducing the annotation cost of samples Finally, the recognition results of surface vegetation cover types in the study area are presented, and the model’s accuracy is verified through field investigation.

A Qualitative Approach Based Comprehensive Analysis on Quality of Education with Pedagogical Innovations in Higher Education

One of the mainstays of progressing in society is higher levels of education. Because of this, and many other recent academic and technical breakthroughs, radical approaches to higher education are now possible. A number of problems, however, arise within this model concerning the potential for a direct link between innovation and the quality that students perceive in higher education. Therefore, this research aims to analyze theconnection, identifying the essential features of educational innovation that affect quality, and demonstrating various practices along these lines. In order to investigate how students and academicians feel about the active learning strategies already in place, this research takes a qualitative approach. Students and academicians were the target audience for a closed-ended analysis used to gather the data. The unforeseen impact of the global pandemic on higher education has prompted a thorough reevaluation of all aspects of the current system, including how to make better use of technology in the classroom. Although it is known that bringing lessons online can be accomplished swiftly, there are significant discrepancies in early claims regarding quality, acceptance, completion, and learning. Therefore, it is essential to review the current technological pedagogical innovations and practices. This research employs an exploratory approach to analyze contemporary technical, organizational, and educational trends and difficulties in an effort to better comprehend the full scope of the problem. The development of integrated academics, with an emphasis on both research and practice to update pedagogical activities, requires significant investment if the quality of instructions and learning in higher education are to be improved. This research aims to briefly analyze the methodological techniques to check if the novel pedagogical practices are being used in higher education institutions and their impact on quality of education with their adoption.

Combining Problem-Based Learning and Team-Based Learning in a Longitudinal Integrated Clerkship: How Do Students Spend Their Self-Study Time?

Self-study is essential for student learning, and active learning methods aim to facilitate constructive self-study time usage. How active learning strategies actually affect student self-study in medical education, however, remains partly unknown. The aim of this study was to examine medical students' use of self-study time in a longitudinal integrated clerkship employing active learning strategies. Week-long time logs were used to collect quantitative data on students' scholastic activities. Furthermore, semistructured individual interviews were conducted for in-depth exploration of different learning activities' influence on students' self-study behaviour. Preparation for and follow-up work after scheduled learning activities were specifically addressed. Students spent on average 42.1 h per week on educational activities, of which self-study comprised 23 h. Preparation for upcoming learning activities was highly prioritized by the students. Team-based learning (TBL) generated most self-study hours, closely followed by patient encounters, while problem-based learning (PBL) induced least amounts of self-study. 50% of self-study time was done without specific relation to scheduled learning activities. Active learning methods appear to promote preparatory work, in accordance with their pedagogic theory. Type of learning activity appears to be decisive in determining the amount of self-study students choose to undertake. Future research should address how quantity and content of different learning activities influence self-study behaviour.

Systematic Literature Review: Strategies for Active and Creative Learning in Elementary School Physical Education

Physical education in elementary schools has an important role in developing children's physical, mental and social development. This research investigates innovative teaching strategies in physical education in elementary schools, with the aim of improving motor skills, increasing physical fitness, and fostering a healthy lifestyle among children. The method used is Systematic Literature Review (SLR) with due diligence articles using MMAT to identify articles during the screening and data extraction stages. Of the 210 articles that were first downloaded, the final 33 articles underwent inclusion and exclusion screening based on certain criteria to analyze various approaches, including technology integration, educational games, skills-based learning, active student participation, and inclusive practices. Findings demonstrate the effectiveness of these strategies in creating engaging and inclusive learning environments. This research highlights the importance of going beyond traditional game-based approaches and exploring more diverse strategies to meet students' individual interests and talents

Motivational Teaching Strategies in EFL Writing Class: Insights from Lecturer Perceptions

The implementation of motivational strategies not only promotes a positive attitude towards writing but also encourages more effective development of students’ writing skills. This study explores lecturers’ insights on how they perceive the motivational teaching strategies in their English writing class. This study employs a qualitative approach with two English writing lecturers from Maros Muslim University as participants. Semi-structured interviews and classroom observation were the instruments used to collect the data and the data were analyzed using thematic analysis. The results show that both lecturers implement motivational strategies effectively by utilizing positive reinforcement, active learning strategies, and addressing students’ difficulties. Positive reinforcement significantly improves students’ motivation and writing performance. It was strengthened by the observational data that showed both lecturers constantly praised students whenever they responded positively during learning process. Lecturers emphasize that giving active learning strategies can motivate students to engage actively in writing tasks, which can enhance motivation and writing proficiency. The lecturers also acknowledge that addressing students’ difficulties is essential in reducing their anxiety and fostering confidence in writing. Recognizing and responding to students’ difficulties can reduce anxiety and encourage them to share their thoughts more confidently in writing.