Social Media by Indian Universities – Does it convince or confuse International Students in University Choice?
Bibliographic record
Abstract
The advent of information and communications technology has changed the way people and organisations function, communicate, transact, recruit, market business and services. To keep pace with the ever-changing business and marketing trends, organisations have integrated information, tools of communication & the boom of internet technology into their strategies.The education industry is no exception to this challenge. The primary goals in marketing University is to attract prospective students from across the globe. Increasing globalisation, aspiration, movement, ease of financial access is drawing students to apply for higher studies in Universities away from their country of residence. Many new-age studies of youth, teenagers and Gen Y overwhelmingly suggest that they are prolific users of the Internet and in particular social media applications. These factors have led to social media being heavily leveraged by universities to create interest and draw in applicants. Social media tools like Facebook, Instagram, LinkedIn, Quora, Google+, Youtube is integrated with the other traditional media viz print/broadcast media, exhibitions, educational trade fairs et al to reach students from various countries. Social Media plays dual power of attracting student applicants as well as helps engagement to create a loyal online community through sharing content which is found relevant and useful by the new young generation.This research paper examines the role of social media in helping international students choose university for higher studies in India. This study included in-depth interviews and a survey to include 183 students’ International students studying in 6 different cities in India.The research findings indicate that International Students increasingly use social media thus highlighting the role of social media in influencing international students’ choice of university . All in all, this study provides directions for universities to influence students innovatively and strategically. International students depend on social media for supplementary information to strengthen the information gathered through primary influencers like family, friends and alumni.With increasing media clutter it's absolutely essential to ensure media planning for use of social media sharp and appropriate media tools are selected to reach the students and thereby enable them to effectively use it for choice of University. Social media strategy through consumer generated word of mouth and influencer marketing to communicate the experience, safety, acceptance of diverse culture can be leveraged to help the students’ choice process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".