A Study of Educational Technology Startups in India

Abstract

Background: EdTech startups are shaping the Indian education industry with the aim of making education truly interesting. The covid-19 pandemic has affected all businesses except EdTech sector. EdTech industry is among those sectors that has observed rapid growth during covid-19. EdTech startups has witnessed a flood of users, funding, and stakeholders like never before. All these reasons make EdTech an interesting topic to study. In order to know EdTech, this study makes an attempt to carry out sentiment analysis of customer reviews on apps of leading educational technology (EdTech) startups.

Purpose: The objective of this paper is to study the sentiments of people towards different EdTech startups in India. The objective is achieved by conducting sentiment analysis of app reviews of three leading EdTech startups operating in India.

Methods: The study uses qualitative research where descriptive research design is used. Data is collected in the form of consumer app reviews from google play store. Reviews were taken for last six months. Total sample of 750 reviews were taken in the study which include 300 reviews for Byju’s learning app, 220 reviews for Unacademy and 230 reviews for Physicswallah. Purposive sampling is applied in this study. Sentiment analysis techniques is applied with the help of Azure Machine Learning MS Excel add-in.

Results: The results of the study suggest that majority of users have negative sentiments towards Physicswallah app, Byju’s learning app, and Unacademy learner app.

Conclusions: The results of this study would be useful to predict people sentiment about Edtech startups apps. The findings of this study give information to management of Edtech startups about positive or negative opinions of their application users so that they can develop strategies exclusively. Further research can include greater number of reviews and also consider demographic data of the reviewers.

  • Page Number : 1-6

  • Published Date : 2023-04-18

  • Keywords
    App Reviews, Educational Technology Startups, Google Play Store, Sentiment Analysis

  • DOI Number
    10.15415/jtmge/2023.141001

  • Authors
    Dinesh Rawat, Kalpana Rawat, Arti Sharma and Shefali

References

  • Agarwal, B., Mittal, N., Bansal, P., & Garg, S. (2015). Sentiment analysis using common-sense and context information. Computational intelligence and neuroscience2015, 30-30.
  • Al-Otaibi, S., Alnassar, A., Alshahrani, A., Al-Mubarak, A., Albugami, S., Almutiri, N., & Albugami, A. (2018). Customer satisfaction measurement using sentiment analysis. International Journal of Advanced Computer Science and Applications9(2).
  • Ali, K., Dong, H., Bouguettaya, A., Erradi, A., & Hadjidj, R. (2017, June). Sentiment analysis as a service: a social media based sentiment analysis framework. In 2017 IEEE international conference on web services (ICWS) (pp. 660-667). IEEE.
  • Bhandari, M., & Rodgers, S. (2020). What does the brand say? Effects of brand feedback to negative eWOM on brand trust and purchase intentions. In Electronic Word of Mouth as a Promotional Technique (pp. 125-141). Routledge.
  • Bargavi, R., & Shanmugam, K. (2023). EdTech industry in India: Revolution and challenges in the Indian market: Teaching case study. Journal of Information Technology Teaching Cases, 20438869231189526.
  • Chatterjee, P. (2001), “Online reviews: do consumers use them?”, In ACR 2001 Proceedings, eds. M. C. Gilly and J. Myers-Levy, Provo, UT: Association for Consumer Research, pp. 129-134.
  • Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science161, 707-714.
  • Ebrahimi, M., Yazdavar, A. H., & Sheth, A. (2017). Challenges of sentiment analysis for dynamic events. IEEE Intelligent Systems32(5), 70-75.
  • Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels-An empirical analysis. Tourism Management61, 43-54.
  • Ghasemaghaei, M., Eslami, S. P., Deal, K., & Hassanein, K. (2018). Reviews’ length and sentiment as correlates of online reviews’ ratings. Internet Research28(3), 544-563.
  • Gupta, S., Sharma, J., Najm, M., & Sharma, S. (2020). Media Exaggeration And Information Credibility: Qualitative Analysis Of Fear Generation For Covid-19 Using Nvivo. Journal of Content, Community and Communication, 14-20.
  • Gursoy, U. T., Bulut, D., & Yigit, C. (2017). Social media mining and sentiment analysis for brand management. Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology3(1), 497-551.
  • Hassan, A. U., Hussain, J., Hussain, M., Sadiq, M., & Lee, S. (2017, October). Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 138-140). IEEE.
  • Jäger, M., Falk, S., & Lenz, T. (2021). Innovative Business Models for Higher Education: An Exploratory Analysis on Education Technology Start-Ups in Selected Countries.
  • Jalilvand, M. R., Esfahani, S. S., & Samiei, N. (2011). Electronic word-of-mouth: Challenges and opportunities. Procedia Computer Science3, 42-46.
  • Joyce, B., & Deng, J. (2017, November). Sentiment analysis of tweets for the 2016 US presidential election. In 2017 ieee mit undergraduate research technology conference (urtc) (pp. 1-4). IEEE.
  • Kang, M., J. Ahn, & K. Lee. (2018) “Opinion Mining Using Ensemble Text Hidden Markov Models for Text Classification.” Expert Systems with Applications 94: 218-227.
  • Lakshmi, V., Harika, K., Bavishya, H. & Harsha, C.S. (2017). Sentiment Analysis of Twitter Data. International Research Journal of Engineering and Technology, 4(2). 2224-2227.
  • Li, M., Ch’ng, E., Chong, A. Y. L., & See, S. (2018). Multi-class Twitter sentiment classification with emojis. Industrial Management & Data Systems118(9), 1804-1820. doi: 10.1108/ IMDS-12-2017-0582.
  • Lin, C. A., & Xu, X. (2017). Effectiveness of online consumer reviews: The influence of valence, reviewer ethnicity, social distance and source trustworthiness. Internet Research27(2), 362-380.
  • Lufungulo, E. S., Jia, J., Mulubale, S., Mambwe, E., & Mwila, K. (2023). Innovations and Strategies During Online Teaching in an EdTech Low-Resourced University. SN Computer Science4(4), 328.
  • Mansour, S. (2018). Social media analysis of user’s responses to terrorism using sentiment analysis and text mining. Procedia Computer Science140, 95-103.
  • Miley, F., & Read, A. (2011). Using word clouds to develop proactive learners. Journal of the Scholarship of Teaching and Learning, 11(2), 91-110.
  • Mukherjee, S., & Ganguly, U. (2021). Edtech Startups in India: Leveraging the New Normal. Academia Letters, 2.
  • Nasukawa, T., & Yi, J. (2003). Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (pp. 494–500).
  • Naz, F. (2015), Word of Mouth and Softdrink, Vol. 4 No. 1, pp. 1-4.
  • PGA Labs (2020), “The great ‘un-lockdown’: Indian EdTech – disruptions and opportunities for the next decade”, available at: https://www.praxisga.com/PraxisgaImages/ReportImg/pga-labsivca-report-the-great-un-lockdown-indian-edtech-Report-3.pdf
  • Ragini, J. R., Anand, P. R., & Bhaskar, V. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management42, 13-24.
  • Sharma, R., Morales-Arroyo, M., & Pandey, T. (2012). The emergence of electronic word-of-mouth as a marketing channel for the digital marketplace. Ravi S. Sharma, Miguel Morales-Arroyo, and Tushar Pandey.“The Emergence of Electronic Word-of-Mouth as a Marketing Channel for the Digital Marketplace”, Journal of Information, Information Technology, and Organizations6, 41-61.
  • Singla, Z., Randhawa, S., & Jain, S. (2017, June). Sentiment analysis of customer product reviews using machine learning. In 2017 international conference on intelligent computing and control (I2C2) (pp. 1-5). IEEE.
  • Watson, J., Baier, J., Mughogho, W., & Millrine, M. (2023). An exploratory investigation into the factors related to EdTech use among Kenyan girls. British Journal of Educational Technology, 54(4), 1006-1024.
  • Widyaningrum, P., Ruldeviyani, Y., & Dharayani, R. (2019). Sentiment Analysis to Assess the Community’s Enthusiasm towards the Development Chatbot Using an Appraisal Theory. Procedia Computer Science161, 723-730.
  • Yan, D., & Li, G. (2023). A Heterogeneity Study on the Effect of Digital Education Technology on the Sustainability of Cognitive Ability for Middle School Students. Sustainability15(3), 2784.
  • Yoon, Y., Kim, A. J., Kim, J., & Choi, J. (2019). The effects of eWOM characteristics on consumer ratings: evidence from TripAdvisor. com. International Journal of Advertising38(5), 684-703.