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 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>

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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