Matching Items (15)

Fad Diets and Evidence-Based Research: 3 Mini-Case Studies in Student-Driven How-To Research Sessions

Description

As a Health Sciences Librarian at a large public research university, requests for one off library sessions, or online how-to support, to teach evidence-based practice (EBP) research skills are common.

As a Health Sciences Librarian at a large public research university, requests for one off library sessions, or online how-to support, to teach evidence-based practice (EBP) research skills are common. Having mastered brief 'hands-on' activities to practice skills learned, I was ready to branch out, and so were some faculty with whom I work, especially in the fields of Nutrition, Exercise, and Wellness. For Spring 2013 I worked with faculty to try pre-class time assignments followed by participatory, hands-on, student reporting (flipped) class sessions on:

1. Finding the source of research reported in health news articles.
2. Identifying high level EBP research studies on a nutrition topic.
3. Exploring career and research tools in Kinesiology.

This session will include a brief overview of each case study with discussion opportunities.

Contributors

Agent

Created

Date Created
  • 2014-05-13

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Increased interactions in active learning biology classrooms: Exploring the impact of instructors using student names and student academic self-concept

Description

Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in

Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in the context of a high-enrollment active-learning undergraduate biology course. Using surveys and semistructured interviews, we investigated whether students perceived that instructors know their names, the importance of instructors knowing their names, and how instructors learned their names. We found that, while only 20% of students perceived their names were known in previous high-enrollment biology classes, 78% of students perceived that an instructor of this course knew their names. However, instructors only knew 53% of names, indicating that instructors do not have to know student names in order for students to perceive that their names are known. Using grounded theory, we identified nine reasons why students feel that having their names known is important. When we asked students how they perceived instructors learned their names, the most common response was instructor use of name tents during in-class discussion. These findings suggest that students can benefit from perceiving that instructors know their names and name tents could be a relatively easy way for students to think that instructors know their names. Academic self-concept is one's perception of his or her ability in an academic domain compared to other students. As college biology classrooms transition from lecturing to active learning, students interact more with each other and are likely comparing themselves more to students in the class. Student characteristics, such as gender and race/ethnicity, can impact the level of academic self-concept, however this has been unexplored in the context of undergraduate biology. In this study, we explored whether student characteristics can affect academic self-concept in the context of a college physiology course. Using a survey, students self-reported how smart they perceived themselves in the context of physiology compared to the whole class and compared to the student they worked most closely with in class. Using logistic regression, we found that males and native English speakers had significantly higher academic self-concept compared to the whole class compared with females and non-native English speakers, respectively. We also found that males and non-transfer students had significantly higher academic self-concept compared to the student they worked most closely with in class compared with females and transfer students, respectively. Using grounded theory, we identified ten distinct factors that influenced how students determined whether they are more or less smart than their groupmate. Finally, we found that students were more likely to report participating less than their groupmate if they had a lower academic self-concept. These findings suggest that student characteristics can influence students' academic self-concept, which in turn may influence their participation in small group discussion.

Contributors

Agent

Created

Date Created
  • 2017-05

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Using an Active Case Based Learning Model to Increase Scientific Interest, Understanding of and Confidence in the Process of Science in Secondary Education

Description

Many high school students demonstrate an overall lack of interest in science. Traditional teaching methodologies seem to be unsuccessful at engaging students \u2014 one explanation is that students often interpret

Many high school students demonstrate an overall lack of interest in science. Traditional teaching methodologies seem to be unsuccessful at engaging students \u2014 one explanation is that students often interpret what they learn in school as irrelevant to their personal lives. Active learning and case based learning methodologies seem to be more effective at promoting interest and understanding of scientific principles. The purpose of our research was to implement a lab with updated teaching methodologies that included an active learning and case based curriculum. The lab was implemented in two high school honors biology classes with the specific goals of: significantly increasing students' interest in science and its related fields; increasing students' self-efficacy in their ability to understand and interpret the traditional process of the scientific method; and increasing this traditional process of objectively understanding the scientific method. Our results indicated that interest in science and its related fields (p = .011), students' self-efficacy in understanding the scientific method (p = .000), and students' objective understanding of the scientific method (p = .000) statistically significantly increased after the lab was administered; however, our results may not be as meaningful as the p-values imply due to the scale of our assessment.

Contributors

Agent

Created

Date Created
  • 2012-12

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Assessment of Concept Mapping in a Biomaterials Class

Description

Concept maps are teaching tools used to encourage students to utilize active learning strategies and to take responsibility for their own learning. The purpose of this two-semester study is to

Concept maps are teaching tools used to encourage students to utilize active learning strategies and to take responsibility for their own learning. The purpose of this two-semester study is to evaluate the use of concept maps in a junior-level Biomaterials classroom. The maps are assessed based on students' attitude, achievement, and persistence. No significant correlation was determined between concept map score and achievement (correlation coefficient = 0.1739 in the first semester, 0.2208 in the first set of the second semester, and 0.0829 in the second set of the second semester), though further studies should be completed to support the effects of concept mapping. Statistically significant increases in student attitude regarding concept mapping cost, interest, and utility between the two semesters were found (p = 0.013, p = 0.105, p = 0.002, p = 0.083 overall). Persistence was moderately high throughout the entire study (98% in the first semester and 100% in the second semester).

Contributors

Created

Date Created
  • 2016-05

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Using a Longitudinal Case Study to Teach Warfarin Concepts to Prelicensure and Postbaccalaureate Nursing Students: A Peer Evaluation

Description

The purpose of this study was to determine whether a peer nursing student who presents a longitudinal case study on warfarin in a pharmacology course classroom influences prelicensure and postbaccalaureate

The purpose of this study was to determine whether a peer nursing student who presents a longitudinal case study on warfarin in a pharmacology course classroom influences prelicensure and postbaccalaureate nursing students' knowledge and perceived knowledge about warfarin. The study was a descriptive design that used a convenience sample of baccalaureate nursing students enrolled in two pharmacology courses. All participating students answered warfarin case-study questions and completed a self-demographic questionnaire, a knowledge pretest and posttest, and a self-efficacy questionnaire after the activity, which evaluated students' knowledge and perceived knowledge on 11 warfarin concepts. For all students (N = 89), the number of correct answers improved significantly between pretests and posttests for Items 2-11 (p < .0001; Wilcoxon signed-rank tests), which evaluated students' knowledge on warfarin's site of action, associated laboratory values, use of vitamin K, and food-drug interactions. However, no significant difference was seen in the number of correct answers for warfarin's mechanism of action. Comparing prelicensure and postbaccalaureate groups by Mann-Whitney tests, no significant difference was seen for pretest total scores (median 7.00, n = 55; median 7.50, n = 34; respectively; p = .399). Similarly, no difference was seen for posttest total scores by groups (prelicensure: median = 9.00, n =54; postbaccalaureate: median = 10.00, n = 32; p = .344). Overall, students in both groups agreed that they could identify and explain all 11 warfarin concepts. The Pearson correlation between the total posttest and total self-efficacy scores for the combined group was .338 (p = .003), demonstrating a low but significant correlation between students' posttest total scores and their perceived warfarin knowledge, as evaluated by the self-efficacy questionnaire.

Contributors

Agent

Created

Date Created
  • 2014-12

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Building adaptive computational systems for physiological and biomedical data

Description

In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and

In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.

Contributors

Agent

Created

Date Created
  • 2013

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Active engagement in medical education

Description

This study investigates the success of a method used to encourage active engagement strategies among community and research faculty in a College of Medicine, and examines the effects of these

This study investigates the success of a method used to encourage active engagement strategies among community and research faculty in a College of Medicine, and examines the effects of these strategies on medical student engagement and exam scores. Ten faculty used suggestions from the Active Engagement Strategies Website (AESW), which explained four strategies that could easily be incorporated into medical education lectures; pause procedure, audience response system, think-pair-share, and muddiest point. Findings from observations conducted during sessions where an active engagement strategy was implemented and when strategies were not implemented, faculty and student surveys, and exam question analysis indicate faculty members found active engagement strategies easy to incorporate, student engagement and exam score means increased when an active engagement strategy was implemented, and students reported perceptions of attaining a higher level of learning, especially when the pause procedure was implemented. Discussion and implications address low cost and easy ways to provide faculty development in medical education that potentially improves the quality of instruction and enhances student outcomes.

Contributors

Agent

Created

Date Created
  • 2017

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Deep Active Learning Explored Across Diverse Label Spaces

Description

Deep learning architectures have been widely explored in computer vision and have

depicted commendable performance in a variety of applications. A fundamental challenge

in training deep networks is the requirement of large

Deep learning architectures have been widely explored in computer vision and have

depicted commendable performance in a variety of applications. A fundamental challenge

in training deep networks is the requirement of large amounts of labeled training

data. While gathering large quantities of unlabeled data is cheap and easy, annotating

the data is an expensive process in terms of time, labor and human expertise.

Thus, developing algorithms that minimize the human effort in training deep models

is of immense practical importance. Active learning algorithms automatically identify

salient and exemplar samples from large amounts of unlabeled data and can augment

maximal information to supervised learning models, thereby reducing the human annotation

effort in training machine learning models. The goal of this dissertation is to

fuse ideas from deep learning and active learning and design novel deep active learning

algorithms. The proposed learning methodologies explore diverse label spaces to

solve different computer vision applications. Three major contributions have emerged

from this work; (i) a deep active framework for multi-class image classication, (ii)

a deep active model with and without label correlation for multi-label image classi-

cation and (iii) a deep active paradigm for regression. Extensive empirical studies

on a variety of multi-class, multi-label and regression vision datasets corroborate the

potential of the proposed methods for real-world applications. Additional contributions

include: (i) a multimodal emotion database consisting of recordings of facial

expressions, body gestures, vocal expressions and physiological signals of actors enacting

various emotions, (ii) four multimodal deep belief network models and (iii)

an in-depth analysis of the effect of transfer of multimodal emotion features between

source and target networks on classification accuracy and training time. These related

contributions help comprehend the challenges involved in training deep learning

models and motivate the main goal of this dissertation.

Contributors

Agent

Created

Date Created
  • 2018

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Clicking for the Success of all Students: A Literature Review and Classroom Study Investigating the Possible Differential Impact of Clickers

Description

Clickers are a common part of many classrooms across universities. Despite the widespread use, education researchers disagree about how to best use these tools and about how they impact students.

Clickers are a common part of many classrooms across universities. Despite the widespread use, education researchers disagree about how to best use these tools and about how they impact students. Prior work has shown possible differential impacts of clickers based on demographic indicators, such as age, gender, and ethnicity. To explore these topics a two-part project was designed. First, a literature review was completed focusing on past and current clicker practices and the research surrounding them. Second, original data, stratified by demographic characteristics, was collected on student perceptions of clickers. The literature review revealed that not all uses of clickers are created equal. Instructors in higher education first introduced clickers to enhance traditional pedagogies by simplifying common classroom tasks (e.g. grading, attendance, feedback collection). More recently, instructors pair clickers and novel pedagogies. A review of the identified benefits and drawbacks for students and instructors is provided for both approaches. Instructors can use different combinations of technological competency and pedagogical content knowledge that lead to four main outcomes. When instructors have both technological competency and pedagogical content knowledge, all the involved parties, students and instructors, benefit. When instructors have technological competency but lack pedagogical content knowledge, instructors are the main benefactors. When instructors have pedagogical content knowledge alone, students can benefit, but usefulness to the instructor decreases. When instructors have neither technological competency nor pedagogical content knowledge, no party benefits. Beyond these findings, recommendations are provided for future clicker research. Second, the review highlighted that clickers may have a differential impact on students of different demographic groups. To explore this dynamic, an original study on student views of clickers, which included demographic data, was conducted. The original study does not find significantly different enthusiasm for clickers by demographic group, unlike prior studies that explored some of these relationships. However, white students and male students are overrepresented in the group that does not enjoy clickers. This conclusion is supported by visual observations from the means of the demographic groups. Overall, based on the review of the literature and original research, if instructors pair clickers with validated pedagogies, and if researchers continue to study clicker classrooms, including which students like and benefit from clickers, clickers may continue to be a valuable educational technology.

Contributors

Agent

Created

Date Created
  • 2020

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Minimizing Dataset Size Requirements for Machine Learning

Description

Machine learning methodologies are widely used in almost all aspects of software engineering. An effective machine learning model requires large amounts of data to achieve high accuracy. The data used

Machine learning methodologies are widely used in almost all aspects of software engineering. An effective machine learning model requires large amounts of data to achieve high accuracy. The data used for classification is mostly labeled, which is difficult to obtain. The dataset requires both high costs and effort to accurately label the data into different classes. With abundance of data, it becomes necessary that all the data should be labeled for its proper utilization and this work focuses on reducing the labeling effort for large dataset. The thesis presents a comparison of different classifiers performance to test if small set of labeled data can be utilized to build accurate models for high prediction rate. The use of small dataset for classification is then extended to active machine learning methodology where, first a one class classifier will predict the outliers in the data and then the outlier samples are added to a training set for support vector machine classifier for labeling the unlabeled data. The labeling of dataset can be scaled up to avoid manual labeling and building more robust machine learning methodologies.

Contributors

Agent

Created

Date Created
  • 2017