ISSN 2394-5125
 

Research Article 


TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade.

Abstract
Online learning has become important factor in the developed educational system. In today‟s diverse learning population various options are available for learning. But every candidate has different learning curve, so the online learning should also be variant. For every candidate the learning curve can be plotted with respect to parameters such as interest and learnability. This study proposes a data driven recommendation model which uses the candidates‟ interests, and learning style in order to recommend the course. The recommendation model identifies the both student personality type and his interest domain based on collaborative filtering and k-means clustering respectively. It utilizes real time data scrapped from online course providing platforms such as NPTEL, Coursera, MOOC learning sites, etc. The purpose of this system is to train an individual in a particular direction such that he will become proficient in that particular technology stack. In short the motive is to overcome the problem of being „Jack of all many traits and master of none‟. The detailed solution is discussed in the paper.

Key words: Machine Learning, Recommender Systems, Scrapping engine, Clustering, Proficiency, percentile calculation, Item- Based Collaborative Filtering, Python


 
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How to Cite this Article
Pubmed Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. JCR. 2020; 7(19): 1096-1105. doi:10.31838/jcr.07.19.136


Web Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. http://www.jcreview.com/?mno=104078 [Access: September 14, 2020]. doi:10.31838/jcr.07.19.136


AMA (American Medical Association) Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. JCR. 2020; 7(19): 1096-1105. doi:10.31838/jcr.07.19.136



Vancouver/ICMJE Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. JCR. (2020), [cited September 14, 2020]; 7(19): 1096-1105. doi:10.31838/jcr.07.19.136



Harvard Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade (2020) TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. JCR, 7 (19), 1096-1105. doi:10.31838/jcr.07.19.136



Turabian Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. 2020. TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. Journal of Critical Reviews, 7 (19), 1096-1105. doi:10.31838/jcr.07.19.136



Chicago Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. "TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM." Journal of Critical Reviews 7 (2020), 1096-1105. doi:10.31838/jcr.07.19.136



MLA (The Modern Language Association) Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade. "TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM." Journal of Critical Reviews 7.19 (2020), 1096-1105. Print. doi:10.31838/jcr.07.19.136



APA (American Psychological Association) Style

Ruchira Gaikwad, Aishwarya Yadav, Oorja Shrivastava, Hrishikesh Dol, Pallavi Dhade (2020) TECHNICAL PROFICIENCY ASSESSMENT AND ENHANCEMENT SYSTEM. Journal of Critical Reviews, 7 (19), 1096-1105. doi:10.31838/jcr.07.19.136