scholarly journals Learning Approaches and Coping with Academic Stress for Sustainability Teaching: Connections through Canonical Correspondence Analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 852
Author(s):  
Zaira-Jazmín Zárate-Santana ◽  
María-Carmen Patino-Alonso ◽  
Ana-Belén Sánchez-García ◽  
Purificación Galindo-Villardón
Keyword(s):  
Deep Learning ◽  
Correspondence Analysis ◽  
Canonical Correspondence Analysis ◽  
Teaching And Learning ◽  
Online Survey ◽  
Academic Stress ◽  
Learning Approach ◽  
Learning Approaches ◽  
And Gender ◽  
Education Academic

Learning approaches are factors that contribute to sustainability education. Academic stress negatively affects students’ performances in the context of sustainability teaching. This study analyzed how deep and surface approaches could be related to coping with academic stress and gender. An online survey was completed by 1012 university students. The relationship between gender, sources of stress and learning approaches was examined through a multivariate canonical correspondence analysis. Results showed differences in stress-coping strategies depending on the learning approach used. In both female and male students, academic stress was handled with a deep learning approach. The findings provide implications for professors and highlight the importance of variables such as deep learning and gender in the teaching and learning sustainability process.

scholarly journals THE INFLUENCE OF CONTEXTUAL TEACHING AND LEARNING APPROACH AND STUDENTS’ GENDER ON MATHEMATICS UNDERSTANDING AT GRADE IV SDN ALANG LAWAS, PADANG

2021 ◽  
Vol 5 (4) ◽  
pp. 1134
Author(s):  
Remiswal Remiswal ◽  
Dorisno Dorisno
Keyword(s):  
Factorial Design ◽  
Teaching And Learning ◽  
Mathematical Understanding ◽  
Female Students ◽  
Conventional Approach ◽  
Male Students ◽  
Learning Approach ◽  
Learning Approaches ◽  
Mathematics Understanding ◽  
And Gender

The purpose of this study was to describe differences in the understanding of students who learned through CTL approach and students who learned through the conventional approach, to observe the differences in the Mathematics understanding of male students and female students, and to see the interaction between the learning approach and gender on the mathematics understanding. The type of this research was a Quasi Experimental Design with a factorial design (2x2 factorial design). The population in this study was all fourth grade students at SDN Alang Lawas Padang. The samples were students at SDN 37 Alang Lawas as the control class and students at SDN 20 Alang lawas as the experimental class. The results of the research concluded that (1) there were differences in the mathematical understanding of students who were taught by CTL approach and the students who were taught by a conventional approach; (2) there were differences in the mathematical understanding of male students and female students at grade IV SDN Alang Lawas; and (3) there was no significant interaction between learning approaches and gender in influencing students' mathematical understanding.

scholarly journals Design and evaluation of two blended learning approaches: Lessons learned

2011 ◽  
Vol 27 (8) ◽  
Author(s):  
Wing Sum Cheung ◽  
Khe Foon Hew
Keyword(s):  
Blended Learning ◽  
Teaching And Learning ◽  
Theoretical Foundation ◽  
Graduate Programs ◽  
Lessons Learned ◽  
University Teaching ◽  
Learning Approach ◽  
Learning Approaches ◽  
Communication Tool ◽  
Face To Face

<span>In this paper, we share two blended learning approaches used at the National Institute of Education in Singapore. We have been using these two approaches in the last twelve years in many courses ranging from the diploma to graduate programs. For the first blended learning approach, we integrated one asynchronous communication tool with face to face tutorials, classroom discussions, and a reflection session. For the second blended learning approach, we integrated two asynchronous tools with face to face tutorials in a course. We discuss the theoretical foundation of the two blended learning approaches. In addition, we share insights from these two blended learning approaches, based on the students' data (online postings, questionnaires, reflection logs, and interviews), as well as our own reflections. Finally, we describe and discuss several important lessons learned that could inform the design of future instructional strategies in implementing blended learning in university teaching and learning settings.</span>

scholarly journals The Influence of Architecture Students’ Learning Approaches on their Academic Performance in Two Nigeria Universities

2021 ◽  
Vol 20 (2) ◽  
pp. 137-151
Author(s):  
Gabriel Sen ◽  
Albert Adeboye ◽  
Oluwole Alagbe
Keyword(s):  
Regression Analysis ◽  
Academic Performance ◽  
Deep Learning ◽  
Pilot Study ◽  
Statistical Analyses ◽  
Future Research ◽  
Learning Approaches ◽  
Research Needs ◽  
Heterogeneous Samples ◽  
And Gender

The paper was a pilot study that examined learning approaches of architecture students; variability of approaches by university type and gender and; influence of architecture students’ learning approaches on their academic performance. The sample was 349 architecture students from two universities. Descriptive and statistical analyses were used. Results revealed predominant use of deep learning approaches by students. Furthermore, learning approaches neither significantly differed by university type nor gender. Regression analysis revealed that demographic factors accounted for 2.9% of variation in academic performance (F (2,346) = 6.2, p = 0.002, R2 = 0.029, f2 = 0.029) and when learning approaches were also entered the model accounted for 4.4% of variation in academic performance (F (14,334) =2.2, p =0.009, R2 = 0.044, f2=0.044). Deep learning approaches significantly and positively influenced variation in academic performance while surface learning approaches significantly and negatively influenced academic performance. This implies that architectural educators should use instructional methods that encourage deep approaches. Future research needs to use larger and more heterogeneous samples for confirmation of results.

scholarly journals Training Public Health Professionals to Tackle Women, Children, and Families Health Issues: A “Skill-Based Qualitative Learning Approach”

2021 ◽  
Vol 9 ◽  
Author(s):  
Cecilia Obeng
Keyword(s):  
Public Health ◽  
Health Professionals ◽  
Teaching And Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Health Issues ◽  
Children And Families ◽  
Arduous Task ◽  
The One ◽  
Future Work

Purpose: There are several teaching and learning approaches but finding the one that is appropriate for a particular field or training program is an arduous task. The purpose of this paper is to introduce the “Skill Based Qualitative Learning Approach” (SBQLA) in training health professionals.Description: The SBQLA is a pedagogical approach via which learners are trained in developing qualitative questionnaires and interview skills to learn from experts in the Public Health (PH) field. This teaching approach arms students with interview skills that help them identify and address PH roadblocks and get them authentic information from experts. It also equips them with techniques on how to do formalized presentations and come up with projects and interventions that help mitigate and eliminate drivers of health problems among women, children and families.Assessment: Learners' field experiences are shared in a professional presentation style in a class to help trainees benefit from each other's information and to get formalized feedback on their presentation. Assessment in this learning approach is based on a synthesis and an analysis of data collected from professionals.Conclusion: Findings from this learning approach enables experts to shed light on true stories shared by real and authentic individuals whose faces can be associated with their shared experiences. This learning approach makes it possible for trainees to also initiate projects that help them tackle existing and emerging public health issues in their future work.

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.

Applying Blended Learning in an Industrial Context

2009 ◽  
pp. 213-232
Author(s):  
Christian Bunse ◽  
Christian Peper ◽  
Ines Grützner ◽  
Silke Steinbach-Nordmann
Keyword(s):  
Blended Learning ◽  
Teaching And Learning ◽  
New Technologies ◽  
Positive Impact ◽  
Learning Approach ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Industrial Case Study ◽  
E Learning ◽  
Classroom Education

With the rapid rate of innovation in software engineering, teaching and learning of new technologies have become challenging issues. The provision of appropriate education is a key prerequisite for benefiting from new technologies. Experience shows that typical classroom education is not as effective and efficient as it could be. E-learning approaches seem to be a promising solution but e-learning holds problems such as a lack of social communication or loose control on learning progress. This chapter describes a blended learning approach that mixes traditional classroom education with e-learning and that makes use of tightly integrated coaching activities. The concrete effects and enabling factors of this approach are discussed by means of an industrial case study. The results of the study indicate that following a blended learning approach has a positive impact on learning time, effectiveness and sustainability.

In Search of Greater Understanding: The Impact of Mastery Learning on Social Science Education

2020 ◽  
Vol 48 (2) ◽  
pp. 107-119
Author(s):  
Patrick R. Cundiff ◽  
Olivia McLaughlin ◽  
Katherine Brown ◽  
Keiondra Grace
Keyword(s):  
Social Sciences ◽  
Student Learning ◽  
Teaching And Learning ◽  
Mastery Learning ◽  
Relevant Literature ◽  
Learning Approach ◽  
Learning Approaches ◽  
Social Science Education ◽  
Level Of Understanding ◽  
The Impact

Mastery learning approaches were designed to improve student learning and elevate the level of understanding across a broader swath of students. These approaches operate under the belief that all students are capable of learning if given enough time. Little research has examined the utility or applicability of a mastery learning approach for social sciences outside of research methods courses. This study provides a review of the relevant literature on mastery learning, a discussion of the applicability of this approach to the teaching and learning of social sciences, and a review of the process and results of the conversion of more traditionally organized and taught courses to a mastery learning approach. Overall, our evaluation provides evidence that a mastery learning approach can make a significant impact on student learning.

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.

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