Smart computing based student performance evaluation framework for engineering education

Verma, Prabal; Sood, Sandeep K.; Kalra, Sheetal

doi:10.1002/cae.21849

Cited by 28 publications

(19 citation statements)

References 27 publications

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“…Many papers have taken advantage of this to make predictions about students' behavior or to develop security measures to detect network intrusions. For this reason, some papers used more specific methods in their data collection and analysis techniques: Decision Tree [14][15][16], Random Tree [17], Random Forest [15,17,18], Artificial Neural Network (ANN) [15,18], Convolution Neural Networks (CNNs) [12,19], Naïve Bayes [15,20,21], K-means Clustering [20,22], k-Nearest Neighbor (K-NN) [21,23] and others [24][25][26][27] including Bayesian Network, Graph-based Clustering, Local Binary Patterns Histograms and Multimedia and Agents based Question Answering System (MAQAS) (see Table 4).…”

Section: Search Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Smart Education: A Review and Future Research Directions

Martín

Alario-Hoyos

Kloos

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Research and development often move forward based on buzzwords. New terms are coined to summarize new developments, often with several interpretations and without a formal definition. The term Smart Education has been coined to represent a move forward in technology-enhanced education, but what is behind it? Does it represent something essentially different from the educational technologies used before? In this paper, we do a systematic literature review to understand how this term is used, what the technologies behind it are, and what promises are made. We conclude that although the term is fuzzy, there are indeed several developments available today that can make educational technologies much more adapted to the learner and therefore underpin the learning in a smarter way.

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Section: Search Resultsmentioning

confidence: 99%

“…Regarding virtual environments, the paper by Verma et al [24] is a good example. They defined a system for students to interact with each other and objects related to the course.…”

Section: Developed Systemsmentioning

confidence: 99%

Smart Education: A Review and Future Research Directions

Martín

Alario-Hoyos

Kloos

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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“…As it may be seen in the next references along with this article, according to authors , today's technology is capable of improving students’ skills towards employability through e‐learning. It consists of multiple skill‐set service providers such as learning centers, recruitment companies, institutions, and private training centers, etc.…”

Section: Introductionmentioning

confidence: 88%

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Sood

Singh

2019

Comp Applic In Engineering

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In the present scenario, the state of engineering education worldwide is facing a big challenge of the employability of graduates. Cloud computing technology has changed the educational landscape by allowing educators and administrators to shift to the cloud for making actionable decisions. In this paper, a smart learning framework is proposed where universities/colleges can advocate the management of employability enhancement and start skill-set enhancement courses through e-learning. This proposed framework monitors the academic/skill data of students to classify their employability at the early stage of graduation. In addition, an algorithm is proposed for skill-set assessment to find various clusters of students who lack in required skill-set. The predicted clusters of students can be offered opportunities to improve their required skill-set through e-learning. Further, we used the design science research methodology for conducting online courses. Moreover, the prediction of resource usage of the proposed e-learning framework is calculated to improve its effectiveness. In our experiment, results show that our proposed hybrid classier achieves 96.45% accuracy of classification. The prediction of resource usage offers better opportunities for adaptive resource elasticity. The results depict that by introducing the proposed framework in engineering education; more effective decisions can be made to improve the employability and overall growth of students. K E Y W O R D S employability enhancement, fog/cloud computing, optical network, resource usage pattern prediction, skill-set assessment, smart learning 1 | INTRODUCTION Universities/colleges are under great pressure to make the youth ready for jobs since the market demands for skilled workers. The education system throughout the world requires a smart system that can be monitored, controlled, quantitatively measured, and continuously improved with the collaboration of universities/ learning centers to enhance students' employability potential during their early years of graduation. Students' employability depends on a variety of features, such as marks obtained in high school, diploma, graduation, etc., extra technical competence, or other relevant information [29]. The information collected from various resources regarding their features is to be accurate, well-timed and precise.As it may be seen in the next references along with this article, according to authors [1,3,4,10,35,37,38,40,41], today's technology is capable of improving students' skills towards employability through e-learning. It consists of multiple skill-set service providers such as learning centers, recruitment companies, institutions, and private training centers, etc. It provides a personally customized and effective educational environment that reduces the gap between students' skill-set and industry demand at multiple employability parameters, such as aptitude, reasoning, communication, technical/professional certifications, etc.The potential of cloud computing encourages education syste...

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“…This study presents the work carried out in stages one, two, and three of Figure . In stage one of Informed Exploration, several requirements arose of the various aspects recommended by the literature for generating collaboration in learning environments , highlighting some signs of social behavior and patterns of interaction and participation [12,45]. It is in this phase of the methodology that the category of pedagogical aspects on which the model of application of the game in teaching‐learning processes in face‐to‐face courses is based has been strongly worked on.…”

Section: Methodsmentioning

confidence: 99%

MyMOOCSpace: Mobile cloud‐based system tool to improve collaboration and preparation of group assessments in traditional engineering courses in higher education

Ramírez‐Donoso

Pérez‐Sanagustín

Neyem

2018

Comp Applic In Engineering

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Assessments in traditional university courses continue to be an accepted tool for validating the knowledge acquired in the different subjects. It is in this context that students use a variety of practices to prepare assessments and exams appropriately. On the other hand, in recent years, higher education institutions have been using the Massive Open Online Courses (MOOCs) as an alternative to expand and innovate their traditional classroom practices. In this field, some alternatives have been generated in these platforms to extend their functions beyond the courses proposed. MyMOOCSpace is a cloud‐based tool that improves the interaction of students participating in a MOOC course. However, a variation of this tool allows for improved collaboration and preparation of assessments and/or tests for students in a traditional higher education class. This article presents a study using the MyMOOCSpace tool as an alternative to encourage collaboration and preparation of students to take traditional tests or assessments in the field of higher education. To evaluate this application and demonstrate the impact on the results of the evaluations, a case study is presented in which 40 computer engineering students from a Chilean university participated. The results show that MyMOOCSpace generates a positive increase in student interaction regarding the subjects covered in the course, and that it helped them to study and prepare for the final exam of the subject.

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Smart computing based student performance evaluation framework for engineering education

Cited by 28 publications

References 27 publications

Smart Education: A Review and Future Research Directions

Smart Education: A Review and Future Research Directions

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

MyMOOCSpace: Mobile cloud‐based system tool to improve collaboration and preparation of group assessments in traditional engineering courses in higher education

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