Example of Computer Applications in Engineering Education format
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Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format
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Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format Example of Computer Applications in Engineering Education format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Computer Applications in Engineering Education — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Education #282 of 1319 down down by 29 ranks
Engineering (all) #89 of 297 down down by 20 ranks
Computer Science (all) #74 of 226 down down by 13 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 486 Published Papers | 1429 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 29/06/2020
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Related Journals

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

0.856

40% from 2018

Impact factor for Computer Applications in Engineering Education from 2016 - 2019
Year Value
2019 0.856
2018 1.435
2017 1.153
2016 0.694
graph view Graph view
table view Table view

2.9

38% from 2019

CiteRatio for Computer Applications in Engineering Education from 2016 - 2020
Year Value
2020 2.9
2019 2.1
2018 2.1
2017 2.3
2016 2.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 40% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 38% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.478

21% from 2019

SJR for Computer Applications in Engineering Education from 2016 - 2020
Year Value
2020 0.478
2019 0.395
2018 0.395
2017 0.367
2016 0.438
graph view Graph view
table view Table view

1.14

23% from 2019

SNIP for Computer Applications in Engineering Education from 2016 - 2020
Year Value
2020 1.14
2019 0.93
2018 1.45
2017 1.126
2016 1.001
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 21% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 23% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Computer Applications in Engineering Education

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Wiley

Computer Applications in Engineering Education

Computer Applications in Engineering Education provides a forum for publishing peer-reviewed timely information on the innovative uses of computers, Internet, and software tools in engineering education. Besides new courses and software tools, the CAE journal covers areas tha...... Read More

Engineering

i
Last updated on
29 Jun 2020
i
ISSN
1467-9469
i
Impact Factor
Medium - 0.694
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
apa
i
Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/CAE.20186
Virtual laboratories in engineering education: The simulation lab and remote lab

Abstract:

Computing and communication technology has had a significant impact on the engineering education system. This technology has improved online and collaborative learning. Besides that, it improves the students learning experiences. One of the distinguishing elements of engineering education is the laboratory requirement. In thi... Computing and communication technology has had a significant impact on the engineering education system. This technology has improved online and collaborative learning. Besides that, it improves the students learning experiences. One of the distinguishing elements of engineering education is the laboratory requirement. In this paper, we discuss the current trends and key issues in virtual laboratories-simulation environment laboratories and remote laboratories via the Internet. © 2008 Wiley Periodicals, Inc. Comput Appl Eng Educ 17: 108–118, 2009; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae20186 read more read less

Topics:

Engineering education (59%)59% related to the paper, Collaborative learning (57%)57% related to the paper, The Internet (55%)55% related to the paper
347 Citations
Journal Article DOI: 10.1002/CAE.20456
Web usage mining for predicting final marks of students that use Moodle courses

Abstract:

This paper shows how web usage mining can be applied in e-learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and co... This paper shows how web usage mining can be applied in e-learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several well-known classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre-processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 135–146, 2013 read more read less

Topics:

Web mining (61%)61% related to the paper, Educational data mining (61%)61% related to the paper, Usage data (51%)51% related to the paper
View PDF
266 Citations
Journal Article DOI: 10.1002/CAE.20323
Simulation games in engineering education: A state-of-the-art review
Amit Deshpande1, Samuel H. Huang1

Abstract:

Globalization and advances in information technology has prompted a need to change traditional lecture-based passive learning methodology to an active multi-sensory experiential learning methodology. This article presents a state-of-the-art review of extant applications of simulation games in engineering education. It was con... Globalization and advances in information technology has prompted a need to change traditional lecture-based passive learning methodology to an active multi-sensory experiential learning methodology. This article presents a state-of-the-art review of extant applications of simulation games in engineering education. It was concluded that proper application of simulation games in engineering education would maximize the student's transferability of academic knowledge to the industry. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 399–410, 2011 read more read less

Topics:

Experiential learning (57%)57% related to the paper, Engineering education (54%)54% related to the paper, Passive learning (53%)53% related to the paper, Problem-based learning (51%)51% related to the paper
189 Citations
Journal Article DOI: 10.1002/CAE.21622
Exploring students' learning attitude and achievement in flipped learning supported computer aided design curriculum: A study in high school engineering education
Chih-Yang Chao1, Yuan-Tai Chen2, Kuei-Yu Chuang2

Abstract:

This research aims to explore the benefits of a flipped learning approach for students who are taking an introductory-level curriculum on bridge computer aided design in terms of learning attitude and achievement within the curriculum. In this study, collaborative problem based learning (CPBL) supported by flipped learning, a... This research aims to explore the benefits of a flipped learning approach for students who are taking an introductory-level curriculum on bridge computer aided design in terms of learning attitude and achievement within the curriculum. In this study, collaborative problem based learning (CPBL) supported by flipped learning, a blended learning design, was integrated into a high school bridge computer aided design curriculum. Ninety-one 17-year-old students from two K11 classes were assigned randomly to an experimental group and a control group for the study, respectively. To assess the students' achievements and learning attitudes in the different groups, an 8-week (16 h in total) pre- and post-test quasi-experimental study was designed. The results confirmed the effectiveness of the flipped learning approach. Significant differences were found between the experimental and control group in terms of students' achievements. In the experimental group, students' learning attitudes, motivation and self-evaluation were enhanced. In conclusion, the results show that the flipped learning approach has a positive effect on the transfer of learning. Based on the findings obtained, recommendations for the improvement of future K12 engineering education instruction using the flipped learning approach are provided. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:514–526, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21622 read more read less

Topics:

Experiential learning (66%)66% related to the paper, Cooperative learning (65%)65% related to the paper, Educational technology (64%)64% related to the paper, Blended learning (62%)62% related to the paper, Transfer of learning (55%)55% related to the paper
156 Citations
Journal Article DOI: 10.1002/CAE.22179
Mining opinions from instructor evaluation reviews: A deep learning approach
Aytuğ Onan1

Abstract:

Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research i... Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained. read more read less

Topics:

Sentiment analysis (62%)62% related to the paper, Deep learning (53%)53% related to the paper
149 Citations
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Computer Applications in Engineering Education format uses apa citation style.

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Frequently asked questions

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Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Computer Applications in Engineering Education guidelines and auto format it.

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Yes, the template is compliant with the Computer Applications in Engineering Education guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Computer Applications in Engineering Education?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Computer Applications in Engineering Education citation style.

4. Can I use the Computer Applications in Engineering Education templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Computer Applications in Engineering Education.

5. Can I use a manuscript in Computer Applications in Engineering Education that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Computer Applications in Engineering Education that you can download at the end.

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7. Where can I find the template for the Computer Applications in Engineering Education?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computer Applications in Engineering Education's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's Computer Applications in Engineering Education is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in Computer Applications in Engineering Education, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computer Applications in Engineering Education's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Computer Applications in Engineering Education?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Computer Applications in Engineering Education. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Computer Applications in Engineering Education?

The 5 most common citation types in order of usage for Computer Applications in Engineering Education are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Computer Applications in Engineering Education?

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16. Can I download Computer Applications in Engineering Education in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Computer Applications in Engineering Education Endnote style according to Elsevier guidelines.

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