scholarly journals Learning Approaches Among Medical Undergraduates and Pre-Intern Doctors of A Selected University In Sri Lanka

2021 ◽  
Author(s):  
Sajith Edirisinghe ◽  
Dulmini De Silva ◽  
Indunil Liyanage ◽  
Malith Niluka ◽  
Kasuni Madushika ◽  
...  
Keyword(s):  
Deep Learning ◽  
Study Skills ◽  
Medical Curriculum ◽  
Learning Approach ◽  
Learning Approaches ◽  
Strategic Learning ◽  
Strategic Approach ◽  
Significant Difference ◽  
Medical Undergraduates ◽  
The University

Abstract Background Medical education is constantly evolving to suit the changes in the field. It is a challenge to acquire the necessary knowledge, attitudes and skills within a limited time period in order to become a proficient doctor. This study aims to determine and compare the learning approaches (deep, strategic, superficial) used by medical undergraduates and pre-intern doctors. Methods Learning approaches of 138 pre-clinical medical undergraduates and pre-intern doctors of the University of Sri Jayewardenepura were assessed using the Approaches and Study Skills Inventory for Students (ASSIST) questionnaire. Data were analyzed using SPSS v-16 and Brown-Forsythe test. Results The strategic approach was identified as the predominant learning approach among 138 participants. One hundred and eight (108) participants (78.3%) used this method while 21 (15.2%) and 9 (6.5%) participants used the deep approach and the surface apathetic approach, respectively. Majority of both undergraduates [77.6% (83/107)] and pre-interns [80.6% (25/31)] used the strategic approach. This finding was also consistent between the 2 genders with a majority of males [69.6% (32/46)] as well as females [82.6% (76/92)] following the strategic approach. No significant difference in learning approaches was found to be present between undergraduate and pre-intern groups. Conclusions This study demonstrates that a majority of medical undergraduates and pre-intern doctors prefer the strategic learning approach. This implies that the medical curriculum has not adequately promoted the deep learning approach over the five-year period of studentship and this needs to be addressed in a subsequent curriculum change in order to promote a deep learning approach.

scholarly journals Approaches to Learning among Deaf Students at the University of Education, Winneba

2016 ◽  
Vol 4 (9) ◽  
Author(s):  
Daniel Fobi ◽  
Dr. Alexander M. Oppong
Keyword(s):  
Study Skills ◽  
Teaching And Learning ◽  
Approaches To Learning ◽  
University Education ◽  
Learning Approaches ◽  
Deaf Students ◽  
Approach To Learning ◽  
Strategic Approach ◽  
The University ◽  
Higher Institutions

This survey explored the learning approaches among deaf students at the University of Education, Winneba (UEW), Ghana. Data were gathered from 31 out of 41 undergraduate deaf students. Participants were randomly sampled from levels 100, 200, 300 and 400. Data were gathered through the Approaches and Study Skills Inventory for Students (ASSIST, 1998). Data were analysed using descriptive statistics. Findings of the study suggest that participants preferred strategic approach to learning followed by the deep and surface approaches to learning in that order. The study recommended that further investigation be done using longitudinal study in various higher institutions in Ghana. Such a study should examine whether the approaches to learning among deaf students change over time as they go through their university education. The study recommended that in the teaching and learning process, lecturers in the Department of Special Education, UEW need to take into consideration the learning approaches (deep, surface, and strategic) employ to study and plan their teaching to meet such students and should teach each student since deaf students at the university.

scholarly journals The Effects of Pre-service Teachers’ Cognitive Styles on Learning Approaches

2018 ◽  
Vol 7 (4) ◽  
pp. 285
Author(s):  
Sedat Altıntaş ◽  
İzzet Görgen
Keyword(s):  
Deep Learning ◽  
Academic Success ◽  
Cognitive Style ◽  
Prospective Teachers ◽  
Cognitive Styles ◽  
Learning Approach ◽  
Learning Approaches ◽  
Spring Semester ◽  
Embedded Figures Test ◽  
Significant Difference

The main purpose of the study is to investigate the effects of prospective teachers’ cognitive styles on learning approaches. It is aimed to define whether exist significance differences between defining prospective teachers’ cognitive styles and learning approaches and demographic variables within the scope of the mean purpose. The study, designed according to correlational survey model, was conducted at Mugla Sitki Kocman University, Faculty of Education in the 2014-2015 academic year spring semester. As data collection instruments, “The Group Embedded Figures Test” was administered to define prospective teachers’ cognitive style in the study. On the other hand “The Revised Two Factor Study Process Questionnaire” was used to reveal prospective teachers’ learning approaches. According to the findings, prospective teachers generally have field dependent cognitive style. It is determined that between with prospective teachers’ gender and academic success and cognitive style scores there isn’t any significant difference revealed. However, there is significant difference between branches and cognitive style scores. It has been viewed that prospective teachers prefer deep learning approach generally. There isn’t significant difference between gender and learning approaches yet there is significant difference between learning approaches-branches and academic success. It is also concluded that as prospective teachers’ cognitive styles approaches to field independent, deep learning approach preference of prospective teachers has diminished.

scholarly journals Students' Approaches to Learning the New B. Ed. Programme in Ghana: Evidence from Colleges of Education within Mampong Municipality

2021 ◽  
pp. 278-288
Author(s):  
Moses Azerimi Azewara ◽  
Emma Poku Agyeman ◽  
Joseph Dawson-Ahmoah ◽  
Aaron Adusei ◽  
Eric Twum-Ampofo
Keyword(s):  
Deep Learning ◽  
Student Teachers ◽  
Learning Strategies ◽  
Education Programme ◽  
Learning Approach ◽  
Colleges Of Education ◽  
Learning Approaches ◽  
Cross Sectional ◽  
Single Sex ◽  
Significant Difference

This paper explores the use of surface and deep learning strategies of students in two Colleges of Education within Mampong Municipality: St. Monica's College (Single-Sex College-Female) and Mampong Technical College (Single-Sex College-Male) who were enrolled to read a Four-Year Bachelor of Education programme. Drawing on Biggs et al.'s Revised Study Questionnaire (2001), the study investigates whether student-teachers adopt a predominantly surface or deep learning approach to their studies for the New B. Ed. programme introduced into the Colleges of Education. From February to April 2021, we employed a quantitative cross-sectional design. A self-administered questionnaire was used to collect data from 332 participants (level 200 and 300) who were enrolled to study the four-year B. Ed. programme which was introduced in 2018. One-way analysis of variance (ANOVA) and t-tests were used to examine participants' age, sex and level of study in correlation with their learning approaches (Deep or Surface). A significant difference in the Deep learning approach was found between males and females in both schools (p = 0.47). However, there was no significant difference between age, level and learning approach. Their responses were analysed using descriptive statistics. Findings suggest that student-teachers appear to adopt deep learning strategies in their studies at Colleges of Education and that this approach to learning was used regardless of the discipline in which they were enrolled.

scholarly journals Distribution and academic significance of learning approaches among pre-clinical medical students at Trinity School of Medicine, St Vincent and the Grenadines

2018 ◽  
Vol 15 ◽  
pp. 9
Author(s):  
Keshab Raj Paudel ◽  
Hari Prasad Nepal ◽  
Binu Shrestha ◽  
Raju Panta ◽  
Stephen Toth
Keyword(s):  
Deep Learning ◽  
Medical Students ◽  
Basic Science ◽  
T Test ◽  
Learning Approach ◽  
Learning Approaches ◽  
Surface Approach ◽  
Cronbach Alpha ◽  
School Of Medicine ◽  
Significant Difference

Purpose: Different students may adopt different learning approaches: namely, deep and surface. This study aimed to characterize the learning strategies of medical students at Trinity School of Medicine and to explore potential correlations between deep learning approach and the students’ academic scores. Methods: The study was a questionnaire-based, cross-sectional, observational study. A total of 169 medical students in the basic science years of training were included in the study after giving informed consent. The Biggs’s Revised Two-Factor Study Process Questionnaire in paper form was distributed to subjects from January to November 2017. For statistical analyses, the Student t-test, 1-way analysis of variance followed by the post-hoc t-test, and the Pearson correlation test were used. The Cronbach alpha was used to test the internal consistency of the questionnaire. Results: Of the 169 subjects, 132 (response rate, 78.1%) completely filled out the questionnaires. The Cronbach alpha value for the items on the questionnaire was 0.8. The score for the deep learning approach was 29.4± 4.6, whereas the score for the surface approach was 24.3± 4.2, which was a significant difference (P< 0.05). A positive correlation was found between the deep learning approach and students’ academic performance (r= 0.197, P< 0.05, df= 130). Conclusion: Medical students in the basic science years at Trinity School of Medicine adopted the deep learning approach more than the surface approach. Likewise, students who were more inclined towards the deep learning approach scored significantly higher on academic tests.

scholarly journals DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

2020 ◽  
Vol 34 (01) ◽  
pp. 598-605
Author(s):  
Chaoran Cheng ◽  
Fei Tan ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Large Data ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Learning Approach ◽  
Learning Approaches ◽  
Genomic Variants ◽  
Low Resource ◽  
End To End ◽  
Genomic Variant

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

scholarly journals A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 551-604
Author(s):  
Damien Warren Fernando ◽  
Nikos Komninos ◽  
Thomas Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Techniques ◽  
New Directions ◽  
Encryption Schemes ◽  
Malware Evolution ◽  
Number Of Citations ◽  
Research Studies

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

scholarly journals Emotion analysis of Arabic tweets using deep learning approach

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Massa Baali ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Media Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Approaches ◽  
Social Media Analysis ◽  
Deep Convolutional Neural Networks ◽  
Emotion Analysis ◽  
Classification Tasks

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

Mixed Approaches to Learning in the Flipped Classroom: How Students Approach the Learning Environment

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Margrét Sigrún Sigurðardóttir ◽  
Thamar Melanie Heijstra
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Learning Environment ◽  
Learning Process ◽  
Flipped Classroom ◽  
Approaches To Learning ◽  
Male Students ◽  
Learning Approach ◽  
Learning Approaches ◽  
Surface Approach

Flipped teaching is a trend within higher education. Through flipped teaching the learning environment can be altered by moving the lecture out of the classroom through online recordings, while in-classroom sessions focus on active learning and engaging students in their own learning process. In this paper, we used focus groups comprised of male students in a qualitative research course with the aim of understanding the ways in which we might improve active student engagement and motivation within the flipped classroom. The findings indicated that, within the flipped classroom, students mix surface and deep-learning approaches. The online recordings, which students interact with through a surface approach, can function as a stepping stone toward a deep-learning approach to in-class activities, but only if students come to class prepared. The findings therefore suggest that students must be made aware of the importance of preparation prior to flipped classroom in-class activities to ensure the active learning process is successful. By not listening to the recordings (e.g., due to technological failure, as was the case in this study), students can result in only employing a surface approach.

scholarly journals A Cross-sectional Study Investigating Learning Approaches in Undergraduate Medical Education

2019 ◽  
Author(s):  
Aya Akhras ◽  
Waseem Wahood ◽  
Fatemeh Abdulrahman Amir Rad ◽  
Christopher Tuffnell ◽  
David Davis ◽  
...  
Keyword(s):  
Medical Students ◽  
Medical Curriculum ◽  
Cross Sectional Study ◽  
Learning Approaches ◽  
Strategic Learning ◽  
Sectional Study ◽  
Teaching Approaches ◽  
Undergraduate Medical Students ◽  
Cross Sectional ◽  
Initial Results

Abstract Objective The primary objective of this proof-of-concept cross-sectional study was to identify a framework for appraising the learning-approaches of undergraduate medical students in a competency based medical curriculum and correlating the results with teaching-approaches, as well as academic performance. The study was pursued at MBRU, which is a medical school in the Middle East with an undergraduate entry medical program. Results Our framework was blueprinted using the Approaches and Study Skills Inventory for Students (ASSIST) questionnaire, to which we made some modifications such that the overall cogency of the questionnaire wasn’t affected. Initial results with modified ASSIST at MBRU showed that most of our students adopted Deep or Strategic-learning approaches. This observation is in line with other studies in the literature, which shows that modified ASSIST is a suitable tool for mapping generic learning approaches with teaching approaches. Further, based on the insights from our initial results following the implementation of modified ASSIST, we have considered specific pedagogical strategies, in practice at MBRU, which cater to the generic learning approaches of majority of our undergraduate medical students. These pedagogical approaches, A. Feynman’s Technique; and B. Blended learning strategies, if implemented suitably in a curriculum will transform “Surface-learners” to “Deep/Strategic-learners”.

scholarly journals Changes in approaches to learning over three years of university undergraduate study

2017 ◽  
Vol 5 (2) ◽  
pp. 65 ◽  
Author(s):  
Fiona McDonald ◽  
John Reynolds ◽  
Ann Bixley ◽  
Rachel Spronken-Smith
Keyword(s):  
Undergraduate Students ◽  
Study Skills ◽  
Approaches To Learning ◽  
First Year ◽  
Learning Approaches ◽  
Strategic Learning ◽  
Anatomy And Physiology ◽  
Undergraduate Study ◽  
Science Degree ◽  
Surface Learning

This study aimed to evaluate and compare approaches to learning by a longitudinal cohort of undergraduate students as they progressed from their first to third years of study in anatomy and physiology. The Approaches and Study Skills Inventory for Students (ASSIST) wascompleted at the beginning and end of their first year of university study, and in their final semester. At first year, a surface learning approach predominated; however, at third year, students showed a significant increase in their use of deep and strategic learning approaches compared to first year, although surface learning approaches were retained. The extent to which third-year students took both strategic and deep approaches to learning was positively correlated with their performance on assessment. As students progress through a three-year science degree, they develop deeper and more strategic learning approaches, and assessment and teaching styles probably promote these approaches to learning.

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