Investigating the redundancy effect in multimedia learning on a computer science domain

2010 ◽  
Author(s):  
Riaza Mohd Rias ◽  
Halimah Badioze Zaman
Keyword(s):  
Computer Science ◽  
Multimedia Learning ◽  
Redundancy Effect ◽  
Science Domain

Investigating the citation advantage of author-pays charges model in computer science research: a case study of Elsevier and Springer

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tehmina Amjad ◽  
Mehwish Sabir ◽  
Azra Shamim ◽  
Masooma Amjad ◽  
Ali Daud
Keyword(s):  
Open Access ◽  
Computer Science ◽  
Science Research ◽  
Vital Role ◽  
The Body ◽  
Content Type ◽  
Body Of Knowledge ◽  
Computer Science Research ◽  
Science Domain ◽  
Citation Advantage

PurposeCitation is an important measure of quality, and it plays a vital role in evaluating scientific research. However, citation advantage varies from discipline to discipline, subject to subject and topic to topic. This study aims to compare the citation advantage of open access and toll access articles from four subfields of computer science.Design/methodology/approachThis research studies the articles published by two prestigious publishers: Springer and Elsevier in the author-pays charges model from 2011 to 2015. For experimentation, four sub-domains of computer science are selected including (a) artificial intelligence, (b) human–computer interaction, (c) computer vision and graphics, and (d) software engineering. The open-access and toll-based citation advantage is studied and analyzed at the micro level within the computer science domain by performing independent sample t-tests.FindingsThe results of the study highlight that open access articles have a higher citation advantage as compared to toll access articles across years and sub-domains. Further, an increase in open access articles has been observed from 2011 to 2015. The findings of the study show that the citation advantage of open access articles varies among different sub-domains of a subject. The study contributed to the body of knowledge by validating the positive movement toward open access articles in the field of computer science and its sub-domains. Further, this work added the success of the author-pays charges model in terms of citation advantage to the literature of open access.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the citation advantage of the author-pays charges model at a subject level (computer science) along with four sub-domains of computer science.

Assessment of Multi-Engine Machine Translation for English to Hindi Language (MEMTEHiL)

2016 ◽  
Vol 6 (1) ◽  
pp. 30-45
Author(s):  
Pankaj K. Goswami ◽  
Sanjay K. Dwivedi ◽  
C. K. Jha
Keyword(s):  
Computer Science ◽  
Machine Translation ◽  
Translation Quality ◽  
Science Domain ◽  
Hindi Language ◽  
Interactive Version ◽  
Language Pair

English to Hindi translation of the computer-science related e-content, generated through an online freely available machine translation engine may not be technically correct. The expected target translation should be as fluent as intended for the native learners and the meaning of a source e-content should be conveyed properly. A Multi-Engine Machine Translation for English to Hindi Language (MEMTEHiL) framework has been designed and integrated by the authors as a translation solution for the computer science domain e-content. It was possible by enabling the use of well-tested approaches of machine translation. The humanly evaluated and acceptable metrics like fluency and adequacy (F&A) were used to assess the best translation quality for English to Hindi language pair. Besides humanly-judged metrics, another well-tested and existing interactive version of Bi-Lingual Evaluation Understudy (iBLEU) was used for evaluation. Authors have incorporated both parameters (F&A and iBLEU) for assessing the quality of translation as regenerated by the designed MEMTEHiL.

scholarly journals An Investigation of the Role Programming Support Services Have for Mature Students.

2020 ◽  
Author(s):  
Keith Nolan ◽  
Aidan Mooney ◽  
Amy Thompson ◽  
Mark Noone
Keyword(s):  
Computer Science ◽  
Support Services ◽  
Support Service ◽  
Future Research ◽  
Common Trend ◽  
Science Domain ◽  
Programming Support ◽  
High Level ◽  
Intensive Course ◽  
Programming Ability

Programming support services for introductory programmers have seen a rise in popularity in recent years with third level institutions around the world providing “safe spaces” for students to practice their programming skills and get supports without the risk of being judged by anyone. These services appear in many different structures including Support Centres, Software Studios and help desks. The common trend however is that all the users of these services, in general, report that the service has helped them in their studies and garnered them with more confidence in their ability. This paper examines the role which our Computer Science Centre played for students who attended the support service during an intensive higher diploma course. The intensive course is a 3-week course tailored to students who have previously completed a degree in a field not related to CS, and covers CS1 and CS2 material. The structure and design of the support service is outlined in this paper along with the supports offered. A high-level survey was conducted to investigate the effect of the service on students programming self-efficacy. Study design and methodology are described in detail. Early findings suggest that the support services offered to these students improved their belief in their own programming ability which in turn improved their exam grade outcome. The findings provide valuable evidence to justify future research into the functions of support services with the computer science domain.

scholarly journals PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

2020 ◽  
Author(s):  
Yun He ◽  
Zhuoer Wang ◽  
Yin Zhang ◽  
Ruihong Huang ◽  
James Caverlee
Keyword(s):  
Computer Science ◽  
Domain Knowledge ◽  
Science Domain

scholarly journals Barriers to Digital Literacy: Learning to Program

2020 ◽  
Author(s):  
Andréa Cartile
Keyword(s):  
Computer Science ◽  
Digital Literacy ◽  
Teaching And Learning ◽  
Engineering Students ◽  
Approaches To Learning ◽  
Literacy Learning ◽  
First Year ◽  
Science Domain ◽  
Computer Based ◽  
Learning Programming

There are many challenges associated with teaching and learning computer programming for first year engineering students in non-computer based fields. This paper discusses barriers to acquiring the digital literacy needed to learn end-user programming, or programming as a tool to support activities in a non-computer science domain. The first barrier discussed is the gap in educational curriculum, where the first formal introduction to computer science and programming is found in pre-university preparatory courses. The second barrier is a lack of consensus in approaches to learning programming in online resources. A solution of integrating opportunities to use programming as a tool in existing course curriculum activities is proposed, as a way to improve programming accessibility and allow future engineers to use digital skills to innovate in non-computer based applications.

scholarly journals Student Career Prediction Using Advanced Machine Learning Techniques

2018 ◽  
Vol 7 (2.20) ◽  
pp. 26 ◽  
Author(s):  
K Sripath Roy ◽  
K Roopkanth ◽  
V Uday Teja ◽  
V Bhavana ◽  
J Priyanka
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Recommender Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Science Domain ◽  
Job Role

As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This paper mainly concentrates on the career area prediction of computer science domain candidates.  

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