Active Learning Techniques Research Articles

Rise and Learn: Understanding the Morning Shift from the Perspectives of Students

This study addressed the challenges experienced by morning shift students, focusing on their academic performance and well-being. It examined issues such as disrupted sleep-wake cycles, reduced energy levels, and difficulties maintaining focus during early lectures. External factors, including rush hour traffic, early bedtimes, and persistent morning grogginess, compounded these challenges, contributing to social isolation and limited engagement with complex topics. The study also highlighted difficulties in achieving peak alertness due to late-night commitments, time management struggles, and balancing home responsibilities with academic demands, underscoring the gap in understanding how morning shift schedules impact students holistically. A qualitative research design was employed, using interviews to gather in-depth perspectives from morning shift students. The findings revealed that effective strategies, including consistent sleep patterns, calm evening routines, efficient time management, and proper hydration, significantly enhanced students’ readiness and performance. Personalized approaches, such as active learning techniques, nutrient-rich meals, and time-blocking strategies, were also found to improve focus and sustain energy throughout early classes. The study concluded that addressing morning shift students' academic and well-being challenges requires a comprehensive approach. Integrating lifestyle adjustments, strategic planning, and personalized routines can enhance their learning experiences and overall health. These insights offer valuable implications for educators and administrators in optimizing support systems for morning shift learners.

Thoughts on the Trends in 21st-Century Liberal Arts Education : Seeking Possibility of Collaboration between Universities and Industry

As society becomes increasingly complex and globalized, the demands on higher education to foster versatile, socially responsible graduates have grown. This study examines Japan’s efforts to modernize liberal arts education to better align with societal needs, particularly emphasizing data science, STEAM, digital transformation (DX), and the Sustainable Development Goals (SDGs). Research by the Japan University Accreditation Association highlights a disconnect between university curricula and the skill expectations of industries, which prioritize abilities like logical thinking, problem-solving, and data analysis. Despite reforms, communication between higher education institutions and industries remains insufficient, limiting collaboration on curriculum development and obstructing a cohesive societal impact. The concept of “21st-century liberal arts education” in Japan includes interdisciplinary approaches to address complex issues, fostering communication skills for global engagement, integrating digital literacy for a tech-centered world, and cultivating critical thinking. These principles reflect an educational response to a “VUCA” (volatility, uncertainty, complexity, ambiguity) world, stressing literacies that prepare students to tackle global challenges. Case studies across Japan, the U.S., and the U.K. reveal parallel trends in collaborative education reform and active learning techniques. Japan’s universities have initiated project-based learning (PBL) courses, supported by partnerships with local businesses, that offer students real-world problem-solving experiences and promote “living education”.</br>The study underscores the need for continuous dialogue and mutual understanding between academia and society to bridge gaps between university-led reforms and societal needs. Round-table discussions involving universities, industries, and governmental agencies reveal shared values but also emphasize the necessity for enhanced communication, especially regarding graduates’ competencies. Japan's “Innovation Commons” spaces represent an innovative model for community-university partnerships aimed at solving local issues, with broad implications for sustainable development and regional revitalization.</br>In conclusion, the study advocates for strengthening public engagement in education through information dissemination, deeper societal integration, and collaborative curricular reforms that align with the SDGs and ethical challenges posed by digital advancements. The study recommends expanding ethical, legal, and social issue (ELSI) subjects and advancing financial literacy as critical aspects of civic education. Ultimately, this approach to liberal arts education aims to equip students with practical and ethical tools to contribute positively to society amidst evolving global demands.

Investigating Active Learning based on Dynamic Data Selection techniques for Image Classification

This paper explores the efficacy of Active Learning (AL) techniques, specifically focusing on Dynamic Data Selection (DDS), for improving image classification tasks. AL is a machine learning paradigm that enables the automatic selection of the most informative data samples for annotation, thereby reducing the annotation burden and enhancing model performance. In this study, we investigate the integration of DDS techniques with AL strategies to iteratively select the most informative image samples for model training. We use a fine-tuned VGG16 as the underlying classification model due to their effectiveness in image analysis tasks. Our experimental evaluation involves comparing the performance of fine-tuned VGG16 trained with three AL-based DDS techniques on Arabic sign language dataset. We analyze various DDS strategies, including Random selection, Entropy-based selection, and margin selection to determine their impact on model accuracy and annotation efficiency. The results of our study demonstrate the effectiveness of margin selection method-based AL approach in improving the performance of recognition of 32 hand gestures for Arabic sign language (95.3 \%) while minimizing the annotation effort.

Perception of nursing students and faculty on problem based learning

Introduction: Problem-based learning (PBL) is an active learning technique where learning takes place through solving real-problems that facilitates lifelong learning. Nursing as well as medical students have considered PBL method to be more interesting as compared to conventional teaching. Implementation of problem based method of learning in nursing curriculum is new and the first of its kind in context of Nepal. Thus, this study aimed to find out the perception on PBL among nursing students and faculty. Method: A cross-sectional analytical study was conducted in May-Jun 2023 to assess the perception on PBL among 143 bachelor of nursing students and 20 nursing faculty who were involved in PBL sessions of PAHS, School of Nursing and Midwifery. A convenience sampling was employed through online Google form. The Statistical Package for Social Sciences (SPSS) was used to analyze data. Result: Out of 143 students, 65(45.45%) were first year students and 78(54.55%) were second year students. Regarding perception on PBL, 137(95.80%) of students and 19(95.00%) faculty had positive perception. The highest mean score for the student (3.32±0.68) was found on the statement “PBL promotes students’ self-directed learning on the topic”. Likewise, the highest mean score (3.55±0.51) for the faculty perception was on the statement “PBL promotes students’ participation in the learning process”. Conclusion: This study shows the students and faculty had positive perception on PBL as a teaching learning methods in bachelor level nursing program.

Embedding the Flipped Classroom Approach to Support Student Learning in Animal Handling and Clinical Skills: Practical Classes Throughout a Veterinary Curriculum.

A comprehensive bank of flipped classrooms was developed to help students prepare for animal handling and clinical skills practical classes. Flipped classroom is a type of blended learning. In the context of clinical skills, it is designed to provide students with online learning resources prior to attendance at practical classes. The initiative was catalyzed by the pandemic, and the resources continue to be embedded throughout the curriculum. A team approach was used for the development of the bank, and the design embraced relevant pedagogical frameworks and active learning techniques. Feedback was gathered from a small group of students who completed an online form after each practical class throughout the academic year and wrote a reflective piece at the end of the project. Instructors who delivered practical classes participated in focus group discussions. Students particularly liked flipped classrooms that were well designed with a range of content and interactivity. The main benefits for students of the flipped approach were being more prepared and confident, less anxious, and making better use of in-class time to focus on learning skills. One of the main challenges encountered by instructors was managing a group when some students had not done the prework. A few other issues were mentioned, including when the standardized design template was not followed and the workload involved in continuing to enhance and expand the resources. Teamwork and training were crucial to the successful production of the large bank of flipped classrooms. Sharing our experience with the wider education community, within and beyond our institution, continues to be one of team's aims.

Reliability and Sustainability Integrated Design Optimization for Engineering Structures with Active Machine Learning Technique

Recent strategic plans from many governments share the same emphasis on sustainable development goals. The construction industry is recognized as one of the primary contributors to greenhouse gas emissions. Stimulated by such political and practical requirements, this research aims to develop a reliability and sustainability integrated design optimization (RSIDO) framework for engineering structures. Statistical modelling is conducted which provides probabilistic models for embodied carbon coefficients (ECCs). The proposed RSIDO framework contains three main stages. The first stage solves the classical reliability-based design optimization (RBDO) problem. The second stage conducts the reliability-based embodied carbon quantification to define the sustainable reliability constraint. Lastly, the new sustainable probabilistic design constraint is embedded in the design question. Furthermore, to overcome the computational difficulty, an active-learning extended-support vector regression (AL-X-SVR) technique is proposed to improve the computational efficiency of the proposed framework. The embedded regression algorithm is presented with consolidated theoretical backgrounds. Further, an active learning strategy is implemented to select the learning samples efficiently. The applicability and effectiveness of the proposed AL-X-SVR method are demonstrated using a highly nonlinear-constrained mathematical validation. Additionally, the RSIDO framework is demonstrated through three engineering applications. This research contributes a comprehensive framework that not only addresses reliability and sustainability but also enhances computational efficiency, thereby introducing an innovative approach to design optimization in building engineering.

Integration of Active Learning Techniques for Effective Delivery of Machine Learning Course

Abstract — The field of machine learning (ML) is rapidly advancing and has important implications across a wide variety of industries, spanning from the healthcare industry to the financial sector and beyond. Computers are given the ability to gain information from data and make projections or conclusions through the process of machine learning, which is comprised of complex algorithms, statistical techniques, and data processing procedures. Given the complex nature of machine learning and the fact that it is always evolving, providing it with the appropriate instruction may be a challenging endeavor. In the field of machine learning, the conventional educational methods often consist of theoretical lectures, textbooks, and examples that are predetermined. Despite the fact that these methods provide a fundamental understanding of the concepts underlying machine learning, they typically fail to successfully integrate the theoretical knowledge with the execution of those concepts in the actual world. The fact that this gap exists is a topic of significant concern, given that machine learning is primarily utilized to handle practical concerns and to enhance decision-making that is based on data. It is essential to employ effective instructional and learning strategies such as project-based learning (PBL), flipped classrooms, and case studies-based learning in order to address these challenges and ensure that students are adequately prepared to pursue careers in the field of machine learning. Through an examination of several ML course delivery strategies; this study seeks to address the urgent demand for better ML education. These strategies have the potential to improve the learning outcomes of students by bridging the gap between theoretical comprehension and practical application, as this article demonstrates. Through this exploration, we seek to identify and advocate for teaching practices that can better prepare students for the dynamic and evolving landscape of machine learning. Keywords— Project-Based Learning (PBL); Flipped Classroom; Case Studies-based Learning

Acquisition of Knowledge and Meta-Skills Through the Case Method in Politics and Law Classrooms: New Empirical Insight from Japan

The case method has increasingly become one of the most popular active learning techniques. However, empirical research on its effectiveness is predominantly focused on business fields in Western countries and is typically assessed using a few learning outcomes, with little consideration for students’ individual characteristics. This paper provides new empirical insights by examining the effect of various factors on Japanese students’ acquisition of knowledge and vital meta-skills in Politics and Law courses. Based on a survey conducted at a Japanese university, our analysis finds that students’ stress levels and interest in the course subject have a statistically significant impact on the acquisition of knowledge and all meta-skills. Other factors, such as experience, course preparation, and personality affect the acquisition of knowledge and some meta-skills.

Optimizing hybrid models for canopy nitrogen mapping from Sentinel-2 in Google Earth Engine

Canopy nitrogen content (CNC) is a crucial variable for plant health, influencing photosynthesis and growth. An optimized, scalable approach for spatially explicit CNC quantification using Sentinel-2 (S2) data is presented, integrating PROSAIL-PRO simulations with Gaussian Process Regression (GPR) and an Active Learning technique, specifically the Euclidean distance-based diversity (EBD) approach for selective sampling. This hybrid method enhances training dataset efficiency and optimizes CNC models for practical applications. Two GPR models based on PROSAIL-PRO variables were evaluated: a protein-based model (Cprot-LAI) and a chlorophyll-based model (Cab-LAI). Both models, implemented in Google Earth Engine (GEE), demonstrated strong performance and outperformed other machine learning methods, including kernel ridge regression, principal component regression, neural network, weighted k-nearest neighbors regression, partial least squares regression and least squares linear regression. Validation results showed moderate to good accuracies: NRMSECprot−LAI = 16.76%, RCprot−LAI2 = 0.47; NRMSECab−LAI = 18.74%, RCab−LAI2 = 0.51. The models revealed high consistency for an independent validation dataset of the Munich-North-Isar (Germany) test site, with R2 values of 0.58 and 0.71 and NRMSEs of 21.47% and 20.17% for the Cprot-LAI model and Cab-LAI model, respectively. The models also demonstrated high consistency across growing seasons, indicating their potential for time series analysis of CNC dynamics. Application of the S2-based mapping workflow across the Iberian Peninsula, with estimates showing relative uncertainty below 30%, highlights the model’s broad applicability and portability. The optimized EBD-GPR-CNC approach within GEE supports scalable CNC estimation and offers a robust tool for monitoring nitrogen dynamics.

Pedagogical Strategies for Enhancing Physics Education for Pre-Physical Therapy Students

This study explores instructional strategies designed to improve engagement and learning outcomes in introductory physics courses for non-science majors, focusing on pre-physical therapy (pre-PT) students. Given that many of these students lack a robust mathematical background, the teaching approach aims to bridge this gap through structured problem-solving frameworks, hands-on activities, and contextual applications that link physics principles to health sciences. To address this, a teaching approach was developed over three years in two courses at Widener University in Pennsylvania: College Physics I (Mechanics and Heat) and College Physics II (Electricity and Magnetism). This approach combines structured problem-solving frameworks, real-world applications, hands-on activities, and active learning techniques. By emphasizing the relevance of physics concepts to health sciences, such as using examples from biomechanics and neuroelectric signaling, students are encouraged to connect theory with practical applications in their future careers. The structured problem-solving framework simplifies complex physics problems into manageable steps, fostering both computational and conceptual understanding. Interactive assignments and discussion threads further enhance engagement, while activities focused on scientific communication help students articulate complex ideas clearly. These methods helped students to approach physics as an interconnected field relevant to their future careers rather than isolated quantitative challenges. Assessments, including course evaluations and student feedback, indicate that this approach not only enhances conceptual understanding but also fosters scientific communication skills critical for healthcare professions. The findings contribute to a growing body of research suggesting that tailored physics curricula can significantly improve engagement and success among non-science majors pursuing professional healthcare pathways.

Capturing Dichotomic Solvent Behavior in Solute-Solvent Reactions with Neural Network Potentials.

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in agreement with experiment. These barriers are associated with an ensemble of transition states involving the direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role that dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

Consumption-based approaches in proactive detection for content moderation

Implementing effective content moderation systems at scale is an unavoidable and complex challenge facing technology platforms. Developing systems that automate detection and removal of violative content is fraught with performance, safety and fairness considerations that make their implementation challenging. In particular, content-based systems require large amounts of data to train, cannot be easily transferred between contexts, and are susceptible to data drift. For these reasons, platforms employ a wide range of content classification models and rely heavily on human moderation, which can be prohibitively expensive to implement at scale. To address some of these challenges, we developed a framework that relies on consumption patterns to find high-quality leads for human reviewers to assess. This framework leverages consumption networks, and ranks candidate items for review using two techniques: Mean Percentile Ranking (MPR), which we have developed, and an adaptation of Label Propagation (LP). We demonstrate the effectiveness of this approach to find violative material in production settings using professional reviewers, and on a publicly available dataset from MovieLens. We compare our results with a popular collaborative filtering (CF) baseline, and we show that our approach outperforms CF in production settings. Then, we explore how performance can improve using Active Learning techniques. The key advantage of our approach is that it does not require any content-based data; it is able to find both low- and high-consumption items, and is easily scalable and cost effective to run.

Theories and Practices in Learning and Assessment for Postgraduate Medical Education: A Review.

This literature review explores key theories and practical strategies in postgraduate medical education. It examines essential learning strategies, such asdidactic and experiential teaching methods, structured lesson planning, and models such as Maslow's hierarchy and Kolb's experiential learning cycle. Active learning techniques and feedback models, crucial for guiding medical trainees' growth, are also discussed. The review then shifts focus to assessment, looking at both formative and summative approaches, Miller's pyramid of competence, and Van der Vleuten's utility equation. By evaluating assessment formats, blueprinting, and feedback, this review offers insights into educational strategies that enhance postgraduate medical training.

Using text-scrolling to increase student engagement in the STEM classroom: a collaboration

The benefits of using active-learning approaches to increase student engagement and participation in the HE classroom are well known (Allsop et al., 2020). In STEM disciplines specifically, active-learning approaches can contribute to a reduction in achievement gaps for underrepresented students by promoting self-efficacy and a sense of belonging (Kovarik, Robinson and Wenzel, 2022). Despite these benefits, academics in those disciplines may be reluctant to adopt active learning for reasons such as lack of familiarity or more time spent in traditional, lecture-style delivery (Arnaud, 2021). This presentation reported on the outcome of a small-scale pedagogical innovation in one tutorial of a first-year undergraduate theory module in Chemistry. The aim was to increase students’ classroom engagement in the tutorial by using a text-mapping activity with scrolled research papers (Abegglen et al., 2019), with the additional benefit of introducing tutors to this active-learning technique. The transcript of a professional conversation between a Chemistry lecturer and Learning Developer discussing the approach along with slides outlining a typical session were circulated to tutors a week prior to the timetabled tutorial. Qualitative feedback from tutors was presented to indicate the success of the text-mapping activity as a way of increasing student engagement in the tutorial. Insights were also gained about tutors’ own perceptions of pedagogical value and confidence in adopting such approaches in future. Implications were presented for how the first-year tutorials might be redesigned to include more active learning, as well as how Learning Developers can work in collaboration with academic colleagues in STEM programmes.

Curricular Redundancy in Medical Undergraduate Course: Critical Insights from Various Stakeholders in Medical Institutes in India.

Background Medical education is continuously evolving to keep pace with the dynamic field of medicine. This study addresses the issue of curricular redundancy in medical education, highlighting the necessity for periodic reviews to eliminate outdated or irrelevant topics. Method A descriptive qualitative approach was employed, involving participants from various medical schools across India. An online questionnaire was used to gather data on redundant topics, suggested replacements, and improvements in the curriculum for both didactic and non-didactic subjects, along with inputs related to assessment and evaluation methods. Content analysis was used for thematic identification and qualitative interpretation. Results Out of 71 respondents from a wide geographic distribution, 30% were female, and 70% were male, with an age range of 17 to 36 years. Participants expressed concerns about redundant theoretical (32%) and practical (51%) topics. Qualitative analysis highlighted the need for integrating different subjects and placing a stronger focus on practical clinical skills. Participants emphasized the importance of a curriculum that keeps pace with advancements in medicine, such as genomics and artificial intelligence, while also addressing mental health. Specifically, they suggested combining anatomy and surgery courses, incorporating more active learning techniques, and utilizing ongoing assessments to gauge progress. Conclusion The study highlights the necessity of eliminating curricular redundancy in medical education. Recommendations include developing a flexible curriculum, emphasizing region-specific content, and implementing a formative assessment system. Additionally, the importance of faculty development and stakeholder involvement in curriculum design is emphasized. Key Message Key insights for updating the medical undergraduate curriculum include the following:Developing a flexible curriculum.Emphasizing region-specific content.Implementing formative assessments.

Estimating high-resolution snow depth over the North Hemisphere mountains utilizing active microwave backscatter and machine learning

While ground meteorological stations provide accurate snow depth data, their limited spatial coverage results in observational gaps. Satellites offer long-term, large-scale observations, addressing these gaps. Existing snow depth retrieval algorithms mainly use passive microwave remote sensing data with a 25 km resolution, insufficient for capturing snow depth variability in mountainous areas. This paper introduces active microwave backscatter data and machine learning techniques for high-resolution snow depth estimation. We conducted a preliminary exploration of the relationship between Sentinel-1 backscatter coefficient σ0 and snow depth. Due to factors such as vegetation coverage and underlying soil properties, the relationship between σ0 and snow depth is complex and nonlinear. Consequently, six machine learning models were trained to learn this relationship using σ0 and auxiliary data as input features, with in-situ snow depth serving as the target variable. After extensive validation, the Extreme Random Trees (ERT) model was selected for its high accuracy and stability. Using the ERT model, we generated 500 m-resolution snow depth data for northern hemisphere mountains, then analyzed temporal snow depth variations and altitudinal stratification.

EASE: An enhanced active learning framework for aspect-based sentiment analysis based on sample diversity and data augmentation

Aspect-Based Sentiment Analysis (ABSA) has received considerable attention in recent studies. Powerful pre-trained models were proposed which can be fine-tuned for many Natural Language Processing (NLP) tasks, including ABSA. However, fine-tuning these models needs a relatively large amount of labeled data. In this research, we propose EASE; an active learning framework to minimize manual labeling effort. We extend the active learning technique by incorporating the concept of sample diversity where similar samples are not selected for labeling. Furthermore, we maximize the utility of these samples by incorporating data augmentation. EASE was evaluated on three benchmark ABSA datasets from three different domains. The results show that the reduction of the number of needed labeled samples ranges from 88% to 94% among the three datasets while maintaining accuracy. Our results show that active learning is an effective approach to reduce manual labeling effort while maintaining comparable performance. Moreover, it is possible to reduce the number of labeled data even further by incorporating sample diversity and data augmentation while maintaining performance.

Construction of Al–Si interatomic potential based on Bayesian active learning

Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems, and the prohibitively high computational cost associated with ab initio molecular dynamics simulations. In order to enhance simulation efficiency and accuracy of the Al–Si alloys’ microstructural evolution, this study employs a dynamic active learning technique, FLARE, to develop a non-parametric machine learning potential that combines the high accuracy of density functional theory (DFT) with the efficiency of classical molecular dynamics (MD). Without relying on extensive initial ab initio molecular dynamics data or existing databases, collection of the necessary data is progressively made during the active learning process, thereby constructing a potential capable of accurately simulating the structure and dynamics of high-temperature Al–Si alloys. By comparing with experimental measurements and ab initio molecular dynamics calculations, the high accuracy and computational efficiency of this potential is demonstrated in predicting energy, force, structure, and dynamic properties. The results provide novel theoretical insights for optimizing Al–Si alloy processing and underscore the usefulness of active learning methods in constructing high-accuracy potentials.

Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center

IntroductionData collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients’ medical records and clinical notes. Our study aims to develop a pipeline that combines active learning (AL) and NLP techniques to enhance data extraction in an acute ischemic stroke cohort. Materials and methodsConsecutive acute ischemic stroke patients who received reperfusion therapies at IRCCS Humanitas Research Hospital were included. The Italian NLP Bidirectional Encoder Representations from Transformers (BERT) model was trained with AL to automatically extract clinical variables from electronic health text. Simulated active learning performances were evaluated on a set of labels representing patients’ comorbidities, comparing Bayesian Uncertainty Sampling by Disagreement (BALD) and random text selection. Prognostic models predicting patients’ functional outcomes using Gradient Boosting were trained on manually labelled and semi-automatically extracted data and their performance was compared. ResultsThe active learning process initially showed null performance until around 20% of texts were labelled, possibly due to root layers freezing in the BERT model, yet overall, active learning improves model learning efficiency across most comorbidities. Prognostic modelling showed no significant difference in performance between models trained on manually labelled versus semi-automatically extracted data, indicating effective prediction capabilities in both settings. ConclusionsWe developed an efficient language model to automate the extraction of clinical data from Italian unstructured health texts in a cohort of ischemic stroke patients. In a preliminary analysis, we demonstrated its potential applicability for enhancing prediction model accuracy.

PSVII-28 A comparative analysis on student learning using DNA barcoding in undergraduate biology courses

Abstract DNA barcoding is a growing field and is increasingly used to identify animal and plant specimens down to the species level. This type of molecular work is beneficial for animal science students for its use in identifying insect pests or bacterial infections. However, it is equally important for students outside of STEM concentrations to be exposed to molecular work to develop a better understanding of science and combat the growing distrust in science. The overall goal of this project was to train non-molecular biologists in DNA barcoding using active learning techniques. The objectives of the present study were to 1) expose two groups of students (BSCI145 and BSCI467) to molecular lab work, particularly DNA barcoding, 2) effectively communicate how molecular methods can be applied to ecological purposes, and 3) demonstrate that this molecular method can be used for university-level education at two scales. We hypothesized that students at both the 100 and 400 level would exit this experience with a proportionally deeper understanding of how DNA barcoding works and an appreciation for its applications as a result of the hands-on module. We did not expect there to be a significant barrier between the ability to learn this molecular method of 100 and 400 level students. To assess these hypotheses, students in both courses were given a survey at the beginning and end of the semester with the same or very similar questions to observe how their knowledge of DNA barcoding did or did not change. One semester of BSCI145 and 2 semesters of BSCI467 were sampled (n = 24 100 level students, n = 84 400 level students). Surveys were collected anonymously and evaluated using rubrics to assess “correctness”. An ANOVA revealed statistically significant effects from both the course level (P = 0.004) and the survey scores (P = 0.001), indicating that both course level and basal knowledge contribute to explaining the variance in learning. Additionally, Tukey HSD test results displayed students in BSCI 145 exhibited a significantly greater mean learning change when compared with both BSCI 467 2022 and BSCI 467 2023 (P = 0.008 and P = 0.006 respectively). Finally, a linear regression analysis displayed a significant negative relationship between pre-survey scores and mean learning change (β = -0.850, P = 0.001). This means that students with a greater initial learning reference point will not increase in learning as much when compared with students with a decreased initial learning reference point. In conclusion, we accept our hypotheses that students at different academic levels with different backgrounds can successfully learn molecular techniques when provided hands-on modules. This is relevant to education and extension when working with individuals who may not know hard science.