scholarly journals Preferences for deep-surface learning: A vocational education case study using a multimedia assessment activity

2010 ◽  
Vol 26 (7) ◽  
Author(s):  
Simon Hamm ◽  
Ian Robertson
Keyword(s):  
Deep Learning ◽  
Approaches To Learning ◽  
Management Program ◽  
Learning Preferences ◽  
Learning Approach ◽  
Learning Approaches ◽  
Structured Interviews ◽  
Assessment Activity ◽  
Surface Learning ◽  
Deep And Surface Learning

<span>This research tests the proposition that the integration of a multimedia assessment activity into a Diploma of Events Management program promotes a deep learning approach. Firstly, learners' preferences for deep or surface learning were evaluated using the revised two-factor Study Process Questionnaire. Secondly, after completion of an assessment exercise comprising a multimedia presentation with digital images and oral commentary, the respondents' self-described approaches to learning were collected using semi-structured interviews. Using these two data sets, learners preferred and implemented learning approaches were compared. Results show that whilst the multimedia assessment exercise did not prohibit the adoption of a deep learning approach, it tended to enable the adoption of both deep and surface learning approaches. In addition to informing our understanding of the relationship between deep and surface learning preferences and the implementation of a multimedia assessment item, the data gathered also provide some clues related to the sorts of factors that the respondents considered and how they responded to these factors in their undertaking of the assessment exercise.</span>

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 Active entrepreneurship education and the impact on approaches to learning: Mixed methods evidence from a six-year study into one entrepreneurship educator’s classroom

2020 ◽  
pp. 095042222097531
Author(s):  
Vic Curtis ◽  
Rob Moon ◽  
Andy Penaluna
Keyword(s):  
Deep Learning ◽  
Empirical Evidence ◽  
Positive Impact ◽  
Approaches To Learning ◽  
Entrepreneurship Education ◽  
Learning Approaches ◽  
Student Approaches To Learning ◽  
Approach To Teaching ◽  
Surface Learning ◽  
The Impact

Taking an active and experiential approach to teaching is often assumed to be the best way to promote learning. However, the empirical evidence to support this assertion in entrepreneurship education is inconclusive, and current practice suggests that delivery in higher education is still quite passive and traditional. This 6-year, mixed method study sets out to demonstrate that, in a final-year International Entrepreneurship module at a UK university mapped through the lens of ‘about’, ‘for’ and ‘through’ entrepreneurship, a more innovative, active, experiential and constructively aligned approach to teaching, learning and assessment impacts positively on students’ deep and surface approaches to learning. Students viewed the module as significantly more active than passive and the level of deep learning was significantly greater than the level of surface learning. Additionally, the more active approach was significantly correlated to increased deep learning and reduced surface learning. Students highlighted the active teaching approach and the creation of videos for a local company as part of the authentic assessment as catalysts for deeper learning approaches. The study provides empirical evidence that active entrepreneurship education has a positive impact on student approaches to learning.

scholarly journals Enhancing Students’ Deep Approaches to Learning Among Industrial Mechatronics Engineering Technology Students

2017 ◽  
Vol 9 (1-2) ◽  
Author(s):  
Norsyarizan Shahri ◽  
Roselainy Abdul Rahman ◽  
Noor Hamizah Hussain
Keyword(s):  
Deep Learning ◽  
Cooperative Learning ◽  
Teaching Method ◽  
Approaches To Learning ◽  
Traditional Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Engineering Technology ◽  
Technology Program ◽  
Learning Success

Identification of students learning approaches and its characteristics that contribute to learning success are so important in achieving learning outcomes. A previous study has shown that most of the students use more of surface learning approach instead of deep learning approach in their studies. The study uses a questionnaire, R-SPQ-2F to evaluate students’ surface and deep learning approach. This paper will present findings from a study that was carried out to compare the effect of using cooperative learning and the traditional method among students in Industrial Mechatronics Engineering Technology Program at MARA High Skill College Balik Pulau, Penang, Malaysia. The aim is to investigate the effectiveness of each method. Using a quantitative approach, two groups of students, one group from each respective teaching method, were studied. Analysis of the findings found that using cooperative learning has a more positive effect when compared to the traditional learning.  

Student Learning in an Online Environment

2011 ◽  
pp. 224-234
Author(s):  
Rodney Arambewela
Keyword(s):  
Deep Learning ◽  
Student Learning ◽  
Learning Experience ◽  
Learning System ◽  
Learning Approaches ◽  
Learning Contexts ◽  
Technology In Teaching ◽  
E Learning ◽  
Learning Orientations ◽  
Surface Learning

The increasing class sizes, changing expectations, diversity and mobility of students and the use of computer technology in teaching have challenged universities, world over, to review educational courses and delivery to provide a more satisfying learning experience to students. Understanding how students learn is essential in this process and continuous enquiry into teaching practices for their effectiveness towards enhancement of student learning outcomes is therefore considered a vital strategy. This chapter discusses an exploratory study on the differences in the learning approaches of a group of students in a second year marketing course in an Australian university. E-learning system remains the primary communication and the learning resource of these students. Results indicate that there are no significant differences in the study approaches of students but on average they seem to demonstrate deep learning than surface learning although they may differ in terms of the learning contexts. The study also reveals that in comparison female students and older aged students seem to demonstrate deep learning orientations than surface learning orientations.

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.

scholarly journals International Students’ Motivation and Learning Approach: A Comparison with Local Students

2016 ◽  
Vol 6 (3) ◽  
pp. 678-699
Author(s):  
Kah Loong Chue ◽  
Youyan Nie
Keyword(s):  
Deep Learning ◽  
International Students ◽  
Private Education ◽  
Psychological Factors ◽  
Psychological Needs ◽  
Learning Approach ◽  
Deep And Surface Learning ◽  
Local Students ◽  
Motivation And Learning ◽  
Identified Regulation

Psychological factors contribute to motivation and learning for international students as much as teaching strategies. 254 international students and 144 local students enrolled in a private education institute were surveyed regarding their perception of psychological needs support, their motivation and learning approach. The results from this study indicated that international students had a higher level of self-determined motivation and used a deep and surface learning approach more extensively than local students. Perceived psychological needs support positively predicted intrinsic motivation, identified regulation and a deep learning approach for both groups. There were also differences in the effects of motivation on learning approach between the two groups. Further possibilities for exploration are discussed in this study.

scholarly journals MUHASEBE EĞİTİMİ ALAN ÖĞRENCİLERİN DERİN VE YÜZEYSEL ÖĞRENME YAKLAŞIMLARININ ÇEŞİTLİ DEĞİŞKENLER AÇISINDAN İNCELENMESİ

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Meryem ÖZTÜRK
Keyword(s):  
High School ◽  
Deep Learning ◽  
Significant Relationship ◽  
School Type ◽  
Preference Order ◽  
Learning Approaches ◽  
First Year Students ◽  
Accounting Courses ◽  
Surface Learning ◽  
And Gender

The purpose of the study is to determine whether there is a relationship between the learning approaches of the accounting students and demographic variables, the attendance to accounting courses and the daily repetition of accounting courses. In the scope of the study, a questionnaire was applied to Atatürk University Erzurum Vocational School Accountancy and Tax Applications Program students. According to the results of study, it was determined that there was a statistically significant relationship between the students' deep learning approach and daily repetition of accounting subject, the high school type they graduate and classroom level; it was determined that there was a statistically significant relationship between the students' surface learning tendency and gender, university preference order, attendance to accounting courses and daily repetition of accounting courses. When compared to the first-year students second-year students have higher deep learning tendencies and when compared to the other students, the students who graduated from the Open Education High School have higher than deep learning tendencies. The surface learning tendencies of the students who prefer the program in the first order is higher than those who prefer the program in the next order and the surface learning tendencies of man students is higher than female students. In addition, while deep learning tendency of students who daily repetition of accounting courses have a higher degree than other students, surface learning tendencies are lower.

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