Student Attitudes Regarding Active Learning In Health Professions Microbiology Course

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

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

Simple SummaryPreclinical drug screening experiments for anti-cancer drug discovery typically involve testing candidate drugs against cancer cell lines. This process can be expensive and time consuming since the possible experimental space can be quite huge, involving all of the combinations of candidate cell lines and drugs. Guiding drug screening experiments with active learning strategies could potentially identify promising candidates for successful experimentation. This study investigates various active learning strategies for selecting experiments to generate response data for identifying effective treatments and improving the performance of drug response prediction models. We have demonstrated that most active learning strategies are more efficient than random selection for identifying effective treatments.It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

Abstract: Properly designed active learning instruction using “Hands-on” activities could result in higher student motivation and also higher teacher involvement, particularly when it implies a change from less active learning strategies (lectures, presentations) that are more commonly found in the daily life of students and teachers. Often, the expected learning improvement after these changes in instructional strategies, are assessed by standardized tests that usually assess only the declarative knowledge of the students. Occasionally, the impact of these proposals is measured by means of perception of the students. These ways of assessing fail to address the question of what really students learn in Hands-on sessions and if they really learn “better” or “more” using this kind of approach. Actually, recent research suggest that not every active learning session could promote improvements in learning, particularly in terms of conceptual knowledge and deep understanding. The workshop describes in this paper aims to explore an efficient active learning approach for equilibrium and stability concepts. The activities proposed in this workshop show different ways in which these topics can be addressed in an active way with potentially very different learning results. After analyzing this experience, participants are asked to design another learning activity using approaches that effectively promote the expected learning. Designs and their analysis allow the comprehension of frameworks as Understanding by design or Didactical Engineering for the development of effective active learning sessions. Key words: active learning, engineering education, didactical engineering, problem-based learning, hands-on

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

Seminars and workshops on active learning techniques are being held at many universities. This essay compares traditional classroom interaction with the new active learning approach and offers a personal account of one instructor's attempts to introduce the active learning method within a criminology curriculum. Small group interaction, weekly written assignments, and student's editing of papers are discussed. Overall the instructor found that students responded positively to the new approaches and that their examination and paper grades improved.

Therapists’ active learning increases treatment fidelity, but research is needed on supervisory strategies to engage therapists in active learning. This study used sequential analysis to examine consultant behaviors associated with increased and decreased probability of eliciting therapists’ active learning. The study included 162 consultation sessions from 27 community therapists implementing Attachment and Biobehavioral Catch-up. Consultants’ client discussion, information provision, and modeling were associated with reduced likelihood of active learning. Consultants’ questions, engagement in active learning strategies, use of video, and silence were associated with greater likelihood of therapist active learning. These findings inform supervisors’ attempts to encourage active learning.

Faculty motivations to use active learning among pharmacy educators

Today, vocabulary has a great importance on language learning. Thus, teachers start looking for new ways to teach vocabulary in order to help their students retain new words in target language. Active learning approach has offered teachers a wide range of strategies they can use while teaching vocabulary. “Vocabulary Role Play” is also one of these strategies. In this study, vocabulary role play strategy was conducted with 11th grade students studying at a public music school in Eskisehir, Turkey. The pre-test and post-test results of students were compared in order to see whether a significant change in retention occurred or not. The results of pre-test and post-test indicated that vocabulary role play strategy helped students store the new words. In addition, students expressed that vocabulary role play strategy contributed to their creativity and vocabulary retention. They also said that they had a lot of fun during the activity and found a great opportunity to practice speaking English. All in all, by using this active learning strategy, a better learning environment was provided to the students in an English class. Article visualizations:

Each student has different abilities. There are students who can achieve it without difficulty, but quite a few students experience difficulties in learning. Several problems were found among students, such as laziness, easily discouraged, indifferent, accompanied by an attitude of opposition to teachers, which were part of students' learning problems. This article discusses the analysis of students' learning difficulties in active learning in elementary schools. The aim of this article is to gain a better understanding of what components influence students' ability to use active learning approaches in elementary schools optimally. The method used in this article is a qualitative method with a literature study approach. In reality, each student is often unable to achieve their learning goals or does not achieve changes in behavior as expected, this can indicate that students often experience learning difficulties and this is an obstacle in achieving learning outcomes at school. By knowing students' learning difficulties, they can of course identify and overcome these difficulties by looking for information about their learning problems, adjusting learning methods, providing additional support and using active learning strategies.

As part of an undergraduate pipeline program at our institution for students from underrepresented minorities in medicine backgrounds, we created an intensive four-week medical microbiology course. Team-based learning (TBL) was implemented in this course to enhance student learning of course content. Three different student cohorts participated in the study, and there were no significant differences in their prior academic achievement based on their undergraduate grade point average (GPA) and pre-course examination scores. Teaching techniques included engaged lectures using an audience response system, TBL, and guided self-directed learning. We hypothesized that more active learning exercises, irrespective of the amount of lecture time, would help students master course content. In year 2 as compared with year 1, TBL exercises were decreased from six to three with a concomitant increase in lecture time, while in year 3, TBL exercises were increased from three to six while maintaining the same amount of lecture time as in year 2. As we hypothesized, there was significant (p < 0.01) improvement in performance on the post-course examination in years 1 and 3 compared with year 2, when only three TBL exercises were used. In contrast to the students’ perceptions that more lecture time enhances learning of course content, our findings suggest that active learning strategies, such as TBL, are more effective than engaged lectures in improving student understanding of course content, as measured by post-course examination performance. Introduction of TBL in pipeline program courses may help achieve better student learning outcomes.

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

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

Background and Aim: This study aims to explore the effectiveness of Communicative Language Teaching (CLT) and active learning activities in enhancing English reading comprehension among Prathomsuksa 6 students at Ban Nasee School. Given the challenges students face in reading comprehension, it is essential to develop effective teaching methods to enhance their skills. The researcher is interested in examining whether CLT and active learning activities can enhance students' reading abilities and what students' attitudes are toward these approaches. This study will compare students' reading comprehension abilities before and after teaching English reading using CLT and active learning activities. Additionally, it will assess students' attitudes toward the learning process. Materials and Methods: This study employed a one-group pretest-posttest design to evaluate the effectiveness of Communicative Language Teaching (CLT) and active learning activities in enhancing English reading comprehension among Prathomsuksa 6 students at Ban Nasee School. A total of 13 students were selected through cluster random sampling, and the study followed a one-group pretest-posttest design over 12 weeks. Thirteen students participated in 12 weeks of instruction, with two hours of class per week. The instruments used included 12 lesson plans, an English reading comprehension test, and an attitude questionnaire. Data were analyzed using descriptive statistics (means, percentages, standard deviations) and inferential statistics (one-sample t-tests, dependent samples t-tests) to assess changes in reading comprehension and student attitudes. The findings provide insights into the potential of CLT and active learning to improve student engagement and reading comprehension in English. Results: The results of this study revealed a significant improvement in students' English reading comprehension after the implementation of CLT and active learning activities. The pretest mean score increased from 11.61 (29.05%) to 32.30 (80.77%) in the posttest, exceeding the criterion of 70%. Statistical analysis revealed a significant difference in scores, indicating that the teaching methods effectively enhanced reading comprehension. Additionally, students demonstrated a positive attitude toward the use of CLT and active learning, reflecting high levels of engagement and satisfaction with the learning process. These results suggest the effectiveness of CLT and active learning strategies in improving reading comprehension skills. Conclusion: The implementation of Communicative Language Teaching (CLT) and active learning activities significantly improved English reading comprehension among Prathomsuksa 6 students. The results highlight both increased reading proficiency and positive student attitudes, demonstrating the effectiveness of these teaching methods.

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