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Comparative educational outcomes of an active versus passive learning continuing professional development activity on self-management support for respiratory educators: A non-randomized controlled mixed-methods study
نتایج آموزشی مقایسهای فعالیت توسعه حرفهای مستمر یادگیری فعال در مقابل غیرفعال بر حمایت خودمدیریتی از مربیان تنفسی: یک مطالعه با روشهای ترکیبی کنترلشده غیرتصادفی-2021 Aim: We compared educational outcomes associated with an active vs. passive continuing professional development activity on self-management support for respiratory educators.
Background: There is a need to identify learning activities associated with the most successful continuing professional development programs for respiratory educators.
Design: This was a non-randomized controlled mixed-methods study recruiting respiratory educators attending a
continuing professional development activity on self-management support.
Methods: In the experimental group, active learning methods (role-play simulations) were employed, whereas
passive learning methods (lecture) were used in the comparison group. Educators were allocated to the comparison group (first 15 months of the study), then to the experimental group (last 17 months). Educators filled
questionnaires measuring pre-/post-activity knowledge about self-management support (score 0–25) and selfreported competence (score 1–10). Scores were compared using mixed-effect models. Interviews with educators were conducted and content analysis was performed.
Results: We recruited 94/94 educators (active: n = 51; passive: n = 43). Knowledge scores increased to a greater
extent in the active vs. passive learning group (adjusted difference-in-difference [aDID]=2.01; 95% confidence
interval [95%CI]: 0.14–3.88), although competence scores increased to a greater extent in the passive learning
group (aDID=− 0.38; 95%CI: − 1.56 to − 0.04). Reflecting on their competence, educators of the active learning
group identified the need to further improve their self-management support skills, whereas educators of the
passive learning group did not.
Conclusions: Our results show that an active learning continuing professional development activity on selfmanagement support could help educators to better apply knowledge and appears to engage them in a process of reflection on action.
keywords: Chronic obstructive pulmonary disease | Continuing education | Mixed methods | Patient education as topic | Self-management support |
مقاله انگلیسی |
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Improving learning in the management of gender violence: Educational impact of a training program with reflective analysis of dramatized video problems in postgraduate nurses
بهبود یادگیری در مدیریت خشونت جنسیتی:تأثیر آموزشی یک برنامه آموزشی با تجزیه و تحلیل بازتابنده مشکلات ویدئویی دراماتیک در پرستاران کارشناسی ارشد-2021 Background: Most gender-based violence victims who sought help in Spain did so through health services.
Training on gender-based violence with active learning methodologies promotes the management of knowledge,
reflection, and adaptation to change. Nurses, along with an educator, can construct knowledge with the same
strategies they will use professionally.
Purpose: To evaluate the knowledge, skills, and attitudes associated of postgraduate nurses on gender-based
violence before and after a reflection-based training program with dramatized problem-videos. The secondary
objectives were to evaluate the knowledge in the activation of protocols, skills, and attitudes in the management
of women who are victims of gender-based violence, the consolidation of learning, and the applicability to the
workplace.
Methods: Pre-post quasi-experimental study without a control group. A specifically validated and designed in-
strument was utilized to evaluate the dimensions of knowledge, skills, and attitudes when facing gender-based
violence, before and after the training sessions, along with additional questions to assess if the participants
possessed better tools to address gender-based violence.
Results: The difference between the pre and post-tests was statistically significant for the dimensions knowledge,
skills, and attitude (p < 0.05), with a smaller effect size in the dimensions skills and attitude. Also, high scores
were observed in the consolidation of learning and applicability to the workplace.
Conclusion: Reflection-based training with dramatized problem-videos improved the acquisition of tools neces-
sary for the detection and management of gender-based violence of nurses. keywords: پرستاری آموزش و پرورش | خشونت بر اساس جنسیت | یادگیری فعال | یادگیری آنلاین | ارزیابی کمی | Education nursing | Gender-based violence | Active learning | Online learning | Quantitative evaluation |
مقاله انگلیسی |
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Mediation effect of students’ perception of accounting on the relationship between game-based learning and learning approaches
تأثیر میانجیگری درک دانش آموزان از حسابداری در رابطه بین یادگیری مبتنی بر بازی و رویکردهای یادگیری-2021 This study explores the mediation effect of students’ perceptions toward accounting to
enhance their adoption of the deep learning approach. We adopt game-based learning
(GBL) using a self-developed LEGO simulation game as the active learning material.
Participants of this study comprised undergraduate students majoring in accounting from
a northern university in Italy. This study contributes to the literature by providing several
new insights. First, we present statistical evidence of a mediation effect of students’ perceptions toward accounting on their learning approach, although the course offers a short-time activity. Second, we did not confirm the significance of students’ strong image
of conformity in accounting as a mediator in the relationship between GBL and a surface
approach to learning. We interpret that students hold more favorable images of conformity
to accounting than before taking the GBL course, possibly fostering them to engage with
deep approach processes while adopting appropriate facilitation of active learning.
keywords: یادگیری فعال | یادگیری مبتنی بر بازی | رویکرد یادگیری | ادراک حسابداری | Active learning | Game-based learning | Learning approach | Perceptions of accounting |
مقاله انگلیسی |
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Probabilistic active learning: An online framework for structural health monitoring
یادگیری فعال احتمالی: یک چارچوب آنلاین برای نظارت بر سلامت ساختاری-2019 A novel, probabilistic framework for the classification, investigation and labelling of data is
suggested as an online strategy for Structural Health Monitoring (SHM). A critical issue for
data-based SHM is a lack of descriptive labels (for measured data), which correspond to the
condition of the monitored system. For many applications, these labels are costly and/or
impractical to obtain, and as a result, conventional supervised learning is not feasible.
This fact forces a dependence on outlier analysis, or one-class classifiers, in practical applications,
as a means of damage detection. The model suggested in this work, however,
allows for the definition of a multi-class classifier, to aid both damage detection and identification,
while using a limited number of the most informative labelled data. The algorithm
is applied to three datasets in the online setting; the Z24 bridge data, a machining
(acoustic emission) dataset, and measurements from ground vibration aircraft tests. In
the experiments, active learning is shown to improve the online classification performance
for damage detection and classification. Keywords: Damage detection | Pattern recognition | Semi-supervised learning |Structural health monitoring |
مقاله انگلیسی |
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مقایسه برداشت ها و نگرش های دانشجویان درباره تجربه دستِ اول تنفس سنجی دربرابر یادگیری فعال مبتنی بر کاغذ
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 17 زمینه و هدف: تنفس سنجی اغلب ازنظر فنی برای بیماران چالش برانگیز است. مطالعات قبلی سودهای بالقوه تنفس سنجی را در زمینه داروخانه های جامعه ای نشان داده اند. این مطالعه برداشت ها و نگرش های دانشجویان رشته داروسازی نسبت به انجام دادن تنفس سنجی و نیز اجرای تنفس سنجی در کلینیک ها و داروخانه های جامعه ای ازطریق تجربه کردن تمرینات یادگیری تنفس سنجی دست اول را با تمرینات یادگیری فعالانه مبتنی بر کاغذ مقایسه می کند.
فعالیت آموزشی و شرایط: دانشجویان سال اول (102 نفر) و سال دوم (70 نفر) رشته داروسازی با مواد درسی پیش از کلاس یکسان جهت یادگیری فرآیند تنفس سنجی فراهم آمدند. درطی کلاس، دانشجویان سال اول آزمایشات تنفس سنجی را انجام دادند درحالیکه دانشجویان سال دوم تمرینات یادگیری فعالانه مبتنی بر کاغذ را درباره تنفس سنجی بدون انجام دادن آزمایش انجام دادند. یک ارزیابی برای هر گروه در انتهای کلاس جهت (1) مقایسه برداشت دانشجویان درباره دشواری انجام تنفس سنجی و (2) شناسایی موانع بیمار، کلینیکی و دارویی اجرای آزمایش تنفس سنجی انجام شد.
یافته ها: دانشجویان سال اول این چنین برداشت کردند که انجام تنفس سنجی به صورت قابل توجهی درمقایسه با دانشجویان سال دوم دشوارتر است. هم دانشجویان سال اول و هم سال دوم این چنین برداشت کردند که نحوه قرارگیری صحیح و روش نفس زنی و ناراحتی بیمار دشوارترین بخشهای انجام تنفس سنجی هستند. دانشجویان سال اول (1/91%) درمقایسه با دانشجویان سال دوم (1/54%) به صورت قابل توجهی تنفس سنجی را یک ابزار کمک کننده تر و غیرتعارضی تر برای رصد کردن بیماری های ریوی دانستند.
خلاصه:دانشجویانی که تنفس سنجی را تجربه کردند نسبت به دانشجویانی که تمرینات یادگیری فعال مبتنی بر کاغذ را انجام دادند این چنین براشت کردند که تنفس سنجی دشوارتر است. وارد کردن تنفس سنجی به برنامه درسی رشته داروسازی می تواند فرصتی برای افزایش بینش دانشجویان درباره دشواری انجام دادن تنفس سنجی و افزایش قدردانی آنها از خدمات کلینیکی دارویی باشد.
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مقاله ترجمه شده |
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Active deep learning for the identification of concepts and relations in electroencephalography reports
یادگیری عمیق فعال برای شناسایی مفاهیم و روابط در گزارشات الکتروانسفالوگرافی-2019 The identification of medical concepts, their attributes and the relations between concepts in a large corpus of
Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort
retrieval system. However, the recognition of multiple types of medical concepts, along with the many attributes
characterizing them is challenging, and so is the recognition of the possible relations between them, especially
when desiring to make use of active learning. To address these challenges, in this paper we present the Self-
Attention Concept, Attribute and Relation (SACAR) identifier, which relies on a powerful encoding mechanism
based on the recently introduced Transformer neural architecture (Dehghani et al., 2018). The SACAR identifier
enabled us to consider a recently introduced framework for active learning which uses deep imitation learning
for its selection policy. Our experimental results show that SACAR was able to identify medical concepts more
precisely and exhibited enhanced recall, compared with previous methods. Moreover, SACAR achieves superior
performance in attribute classification for attribute categories of interest, while identifying the relations between
concepts with performance competitive with our previous techniques. As a multi-task network, SACAR achieves
this performance on the three prediction tasks simultaneously, with a single, complex neural network. The
learning curves obtained in the active learning process when using the novel Active Learning Policy Neural
Network (ALPNN) show a significant increase in performance as the active learning progresses. These promising
results enable the extraction of clinical knowledge available in a large collection of EEG reports. Keywords: Deep learning | Electroencephalography | Active learning | Long-distance relation identification | Concept detection | Attribute classification |
مقاله انگلیسی |
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Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning
تشخیص توده سینه از ماموگرافی های دیجیتالی شده اشعه ایکس بر اساس ترکیبی از یادگیری عمیق فعال و یادگیری خود گام-2019 Breast mass detection is a challenging task in mammogram, since mass is usually embedded and
surrounded by various normal tissues with similar density. Recently, deep learning has achieved
impressive performance on this task. However, most deep learning methods require large amounts of
well-annotated datasets. Generally, the training datasets is generated through manual annotation by
experienced radiologists. However, manual annotation is very time-consuming, tedious and subjective.
In this paper, for the purpose of minimizing the annotation efforts, we propose a novel learning
framework for mass detection that incorporates deep active learning (DAL) and self-paced learning
(SPL) paradigm. The DAL can significantly reduce the annotation efforts by radiologists, while improves
the efficiency of model training by obtaining better performance with fewer overall annotated samples.
The SPL is able to alleviate the data ambiguity and yield a robust model with generalization capability
in various scenarios. In detail, we first employ a few of annotated easy samples to initialize the
deep learning model using Focal Loss. In order to find out the most informative samples, we propose
an informativeness query algorithm to rank the large amounts of unannotated samples. Next, we
propose a self-paced sampling algorithm to select a number of the most informative samples. Finally,
the selected most informative samples are manually annotated by experienced radiologists, which
are added into the annotated samples for the model updating. This process is looped until there
are not enough most informative samples in the unannotated samples. We evaluate the proposed
learning framework on 2223 digitized mammograms, which are accompanied with diagnostic reports
containing weakly supervised information. The experimental results suggest that our proposed learning
framework achieves superior performance over the counterparts. Moreover, our proposed learning
framework dramatically reduces the requirement of the annotated samples, i.e., about 20% of all
training data. Keywords: Breast cancer | Mammography | Mass detection | Deep active learning | Self-paced learning |
مقاله انگلیسی |
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I explain, therefore I learn: Improving students’ assessment literacy and deep learning by teaching
من شرح می دهم، بنابراین من یاد می گیرم: بهبود سواد ارزیابی دانش آموزان و یادگیری عمیق با تدریس-2019 Partnership in higher education emphasises an active role for students in both teaching and learning. This
pedagogical culture is likely to make students assessment literate and engage them in deep learning. In this
study, Iranian students experiencing learning-by-teaching (LbT) in private language institutes were interviewed
to compare their perceptions toward assessment and learning with their counterparts without this experience.
Findings show that LbT fosters students’ assessment literacy and deep learning. Results also reveal that by
teaching other students, quasi-teachers promote a broader understanding of assessment and grade practices in
comparison to other students. Unlike their counterparts, quasi-teachers de-emphasised grades and showed a
greater focus on learning. Moreover, explaining the materials to other students provided them with a deeper
cognitive process resulting in deeper learning. These results underscore the perceived importance of partnership
in higher education. Keywords: Learning-by-teaching (LbT) | Assessment literacy | Deep learning | Active learning | Learning approach |
مقاله انگلیسی |
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Joint temporal context exploitation and active learning for video segmentation
استخراج زمینه زمانی مشترک و یادگیری فعال برای تقسیم بندی ویدیو-2019 The segmentation of video, or separating out objects in the foreground, is an important application of pattern recognition and computer vision. Segmentation errors in pattern recognition approaches mainly come from difficulties in selecting maximally informative frames for learning. In this paper, we develop an approach to video segmentation that relies on temporal features by modeling the uncertainty of the distribution of different feature mask forms. We use those uncertainty values for unsupervised active learning. We evaluate our approach on the DAVIS16 annotated video data set and Shining3D dental video data set, and the results show our approach to be more accurate than other video segmentation ap- proaches Keywords: Video segmentation | Deep learning | Computer vision |
مقاله انگلیسی |
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Batch-based active learning: Application to social media data for crisis management
یادگیری فعال مبتنی بر دسته: کاربرد در داده های رسانه های اجتماعی برای مدیریت بحران -2018 Classification of evolving data streams is a challenging task, which is suitably tackled with online learn
ing approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting
its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train
the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL)
to perform the labeling. OBAL is developed for crisis management applications where data streams are
generated by the social media community. OBAL is applied to discriminate relevant from irrelevant so
cial media items. An emergency management user will be interactively queried to label chosen items.
OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of
two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a
labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analy
sis is carried out to show OBAL’s performance, the sensitivity of its parameters, and the contribution of
the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets
related to crises. The empirical results illustrate that OBAL has a very good discrimination power.
Keywords: Online learning ، Active learning ، Classification ، Social media ، Crisis management |
مقاله انگلیسی |