Cited by 59Mobile Learning in EducationTechnology Adoption and User BehaviourTechnology-Enhanced Education Studies

Uses and gratifications of educational apps: A study during COVID-19 pandemic

Open access
Menon, Devadas·2022·Computers and Education Open

Abstract

Although educational apps have emerged as an easily available and accessible alternative to classroom learning, particularly at the time of pandemics like COVID-19, no research has attempted to identify learners intentions behind the usage of different educational apps. The current study developed a valid and reliable research instrument to measure the motivations behind using educational apps. Using the mixed method approach commonly used in uses and gratification (U&G) research, i.e., open-ended essays & national survey (N = 552), this study identified seven gratifications behind learners intention to use educational apps: academic assistance, convenience, entertainment, social influence, novelty, engagement and activity. The result suggests that academic assistance, convenience and social influence were the significant predictors of the intention to use educational apps. The current research also identified the moderating effect of gender in selecting educational apps. One of the most significant contributions of the present study is that it extended the uses and gratification theory applications beyond the traditional media to explain the intention to use educational apps.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.caeo.2022.100076

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Motivations for using ChatGPT were measured with 21 items covering six dimensions: entertainment, novelty, study guide, social influence, active interaction, and engagement.

    “Various types of motivations for using ChatGPT were measured with a total of 21 items covering six dimensions (Menon, 2022): entertainment, novelty, study guide, social influence, active interaction, and engagement.”

    From Motivations and Affordances of ChatGPT Usage for College Students’ Learning (2025) · cites this paper as Menon, 2022

    The Scale development section describes a questionnaire measuring motivations for using educational apps, and the Discussion identifies several motivations that overlap with the cited list. But the paper identifies seven gratifications, including convenience, rather than the claimed six; it does not describe measuring ChatGPT use or establish the claimed 21-item count in the passages found. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 96

  1. Albelali SA, Alaulamie AA. Gender differences in students' continuous adoption of mobile learning in Saudi higher education. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS); 2019. p. 1-5. https://doi.org/10.1109/CAIS.2019.8769554. · DOI
  2. Andone I, Błaszkiewicz K, Eibes M, Trendafilov B, Montag C, Markowetz A. How age and gender affect smartphone usage. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: adjunct; 2016. p. 9-12. https://doi.org/10.1145/2968219.2971451. · DOI · Paper page
  3. Araiza-Alba P, Keane T, Matthews B, Simpson K, Strugnell G, Chen WS, Kaufman J. The potential of 360-degree virtual reality videos to teach water- safety skills to children. Comput & Eduction 2021;163:104096. https://doi.org/ 10.1016/j.compedu.2020.104096. · DOI
  4. Menon D, Meghana HR. Unpacking the uses and gratifications of Facebook: A study among college teachers in India. Computers in Human Behavior Reports 2021;3:100066.
  5. Beldad AD, Hegner SM. Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users' willingness to continue using a fitness app: a structural equation modeling approach. Int J Hum-Comput Interact 2018;34(9): 882-93. https://doi.org/10.1080/10447318.2017.1403220. · DOI
  6. Beyer H, Holtzblatt K. Contextual design: defining customer-centered systems. San Francisco CA: Morgan Kaufmann Publishers Inc; 1998.
  7. Blackwell CK, Lauricella AR, Wartella E. The influence of TPACK contextual factors on early childhood educators' tablet computer use. Comput Educ 2016;98: 57-69. https://doi.org/10.1016/j.compedu.2016.02.010. · DOI
  8. Blitz-Raith AH, Liu J. Interactivity in educational apps for young children: a multimodal analysis. Int J Instr 2017;10(4):237-54. Accessed from: https://files. eric.ed.gov/fulltext/EJ1155600.pdf.
  9. Bomhold CR. Educational use of smartphone technology: a survey of mobile phone application use by undergraduate university students. Program 2013. https://doi.org/10.1108/PROG-01-2013-0003. · DOI
  10. Bradding A, Horstman M. Using the write and draw technique with children. Eur J Oncol Nurs 1999;3(3):170-5. https://doi.org/10.1016/S1462-3889(99)80801- 1. · DOI
  11. Broekman FL, Piotrowski JT, Beentjes HW, Valkenburg PM. A parental perspective on apps for young children. Comput Hum Behav 2016;63:142-51. https://doi.org/10.1016/j.chb.2016.05.017. · DOI
  12. Broekman FL, Piotrowski JT, Beentjes HW, Valkenburg PM. App features that fulfill parents' needs in apps for children. Mobile Media Commun 2018;6(3): 367-89. https://doi.org/10.1177/2050157918759571. · DOI
  13. Byrne BM. Structural equation modelling with AMOS: basic concepts, applications, and programming. Routledge; 2016. https://doi.org/10.4324/ 9781315757421. · DOI · Paper page
  14. Calder N. Apps: appropriate, applicable, and appealing?. Digital games and mathematics learning. Dordrecht: Springer; 2015. p. 233-50. https://doi.org/ 10.1007/978-94-017-9517-3_12. · DOI
  15. Calvert J, Abadia R. Impact of immersing university and high school students in educational linear narratives using virtual reality technology. Comput Educ 2020; 159:104005. https://doi.org/10.1016/j.compedu.2020.104005. · DOI
  16. Camilleri AC, Camilleri MA. Mobile learning via educational apps: an interpretative study. In: Proceedings of the 2019 5th International Conference on education and training technologies; 2019. p. 88-92. https://doi.org/10.1145/ 3337682.3337687. · DOI
  17. Camilleri MA, Camilleri AC. Digital learning resources and ubiquitous technologies in education. Technol Knowl Learn 2017;22(1):65-82. https://doi. org/10.1007/s10758-016-9287-7. · DOI
  18. Camilleri MA, Camilleri AC. The Student's readiness to engage with mobile learning apps. Interact Technol Smart Educ 2019. https://doi.org/10.1108/ITSE- 06-2019-0027. · DOI
  19. Carter S, Yeo ACM. Mobile apps usage by Malaysian business undergraduates and postgraduates. Internet Res 2016. https://doi.org/10.1108/IntR-10-2014-0273. · DOI
  20. Cayton-Hodges GA, Feng G, Pan X. Tablet-based math assessment: what can we learn from math apps? J Educ Technol Soc 2015;18(2):3-20.
  21. Cherner T, Dix J, Lee C. Cleaning up that mess: a framework for classifying educational apps. Contemp Issues Technol Teacher Educ 2014;14(2):158-93.
  22. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika 1951;16(3):297-334.
  23. DeVellis RF. Scale development: theory and applications (Applied social research methods. Thousand Oaks, CA: SAGE; 2016.
  24. Dhawan S. Online learning: a panacea in the time of COVID-19 crisis. J Educ Technol Syst 2020;49(1):5-22. https://doi.org/10.1177/0047239520934018. · DOI
  25. Dhir A, Chen S, Nieminen M. Predicting adolescent Internet addiction: the roles of demographics, technology accessibility, unwillingness to communicate and sought Internet gratifications. Comput Hum Behav 2015;51:24-33. https://doi. org/10.1016/j.chb.2015.04.056. · DOI
  26. Dias P, Brito R. The Tablet Is My Best Friend!": practices and Perceptions of Young Children and Their Parents. Young children's rights in a digital world. Cham: Springer; 2021. p. 17-28. https://doi.org/10.1007/978-3-030-65916-5_2. · DOI
  27. Dias P, Brito R. Criteria for selecting apps: debating the perceptions of young children, parents and industry stakeholders. Comput Educ 2021;165:104134. https://doi.org/10.1016/j.compedu.2021.104134. · DOI
  28. Dicheva D, Dichev C, Agre G, Angelova G. Gamification in education: a systematic mapping study. J Educ Technol Soc 2015;18(3):75-88. https://doi.org/10.1186/ s41239-017-0042-5. · DOI · Paper page
  29. Dubé AK, Alam SS, Xu C, Wen R, Kacmaz G. Tablets as elementary mathematics education tools: are they effective and why. Mathematical learning and cognition in early childhood. Cham: Springer; 2019. p. 223-48. https://doi.org/10.1007/ 978-3-030-12895-1_13. · DOI
  30. Dubé AK, Kacmaz G, Wen R, Alam SS, Xu C. Identifying quality educational apps: lessons from 'top mathematics apps in the Apple App Store. Educ Inf Technol 2020;25:5389-404. https://doi.org/10.1007/s10639-020-10234-z. · DOI
  31. Fabian K, Topping KJ, Barron IG. Mobile technology and mathematics: effects on students' attitudes, engagement, and achievement. J. Comput Educ 2016;3(1): 77-104. https://doi.org/10.1007/s40692-015-0048-8. · DOI
  32. Falloon G. Young students using iPads: app design and content influences on their learning pathways. Comput Educ 2013;68:505-21. https://doi.org/10.1016/j. compedu.2013.06.006. · DOI
  33. Falloon G. Mobile devices and apps as scaffolds to science learning in the primary classroom. J Sci Educ Technol 2017;26(6):613-28. https://doi.org/10.1007/ s10956-017-9702-4. · DOI
  34. Fornell C, Bookstein FL. Two structural equation models: LISREL and PLS, applied to consumer exit-voice theory. J Market Res 1982;19(4):440-52. https://doi.org/ 10.1177/002224378201900406. · DOI
  35. Gan C. Understanding WeChat users' liking behavior: an empirical study in China. Comput Hum Behav 2017;68:30-9. https://doi.org/10.1016/j. chb.2016.11.002. · DOI
  36. Gikas J, Grant MM. Mobile computing devices in higher education: student perspectives on learning with cellphones, smartphones & social media. Internet High Educ 2013;19:18-26. https://doi.org/10.1016/j.iheduc.2013.06.002. · DOI · Paper page
  37. Gupta A, Dogra N, George B. What determines tourist adoption of smartphone apps? J Hospital Tour Technol 2018. https://doi.org/10.1108/JHTT-02-2017- 0013. · DOI
  38. Hair Black W, Babin B, Anderson R. Multivariate data analysis. 6th Ed. Uppersaddle River, NJ: Pearson Prentice Hall; 2006.
  39. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed, a silver bullet. J Mark Theory Pract 2011;19(2):139-52. https://doi.org/10.2753/MTP1069-6679190202. · DOI · Paper page
  40. Hew JJ, Lee VH, Ooi KB, Wei J. What catalyses mobile apps usage intention: an empirical analysis. Ind Manage Data Syst 2015. https://doi.org/10.1108/IMDS- 01-2015-0029. · DOI
  41. Hirsh-Pasek K, Zosh JM, Golinkoff RM, Gray JH, Robb MB, Kaufman J. Putting education in "educational" apps: lessons from the science of learning. Psychol Sci Public Interest 2015;16(1):3-34. https://doi.org/10.1177/1529100615569721. · DOI
  42. Hoareau L, Thomas A, Tazouti Y, Dinet J, Luxembourger C, Jarlégan A. Beliefs about digital technologies and teachers' acceptance of an educational app for preschoolers. Comput Educ 2021:104264. https://doi.org/10.1016/j. compedu.2021.104264. · DOI
  43. Howard SK, Yang J, Ma J, Maton K, Rennie E. App clusters: exploring patterns of multiple app use in primary learning contexts. Comput Educ 2018;127:154-64. https://doi.org/10.1016/j.compedu.2018.08.021. · DOI
  44. Jeno LM, Vandvik V, Eliassen S, Grytnes JA. Testing the novelty effect of an m- learning tool on internalisation and achievement: a self-determination theory approach. Comput Educ 2019;128:398-413. https://doi.org/10.1016/j. compedu.2018.10.008. · DOI
  45. Kaplan B, Maxwell JA. Qualitative research methods for evaluating computer information systems. In: Anderson JG, Aydin CE, editors. Evaluating the organizational impact of healthcare information systems. Health informatics. New York, NY: Springer; 2005. https://doi.org/10.1007/0-387-30329-4_2. · DOI
  46. Kaur P, Dhir A, Chen S, Malibari A, Almotairi M. Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective. Telemat Inform 2020; 53:101376. https://doi.org/10.1016/j.tele.2020.101376. · DOI
  47. Kearney M, Burden K, Schuck S. Evaluating educational apps from a sociocultural perspective. Theorising and implementing mobile learning. Singapore: Springer; 2020. p. 129-51. https://doi.org/10.1007/978-981-15-8277-6_10. · DOI
  48. Khadir F, Ravindranath V, Sen R. Factors that influence users in selecting mobile apps-a study on Facebook and Facebook Lite. J Public Value Adm Insight 2021;4 (1):24-36. https://doi.org/10.31580/jpvai.v4i1.1571. · DOI
  49. Kim YH, Kim DJ, Wachter K. A study of mobile user engagement (MoEN): engagement motivations, perceived value, satisfaction, and continued engagement intention. Dec Supp Syst 2013;56:361-70. https://doi.org/10.1016/ j.dss.2013.07.002. · DOI
  50. Kinnunen P, Simon B. Phenomenography and grounded theory as research methods in computing education research field. Comput Sci Educ 2012;22(2): 199-218. https://doi.org/10.1080/08993408.2012.692928. · DOI
  51. Kucirkova N, Messer D, Sheehy K, Panadero CF. Children's engagement with educational iPad apps: insights from a Spanish classroom. Comput Educ 2014;71: 175-84. https://doi.org/10.1016/j.compedu.2013.10.003. · DOI
  52. Lee SJ, Ward KP, Chang OD, Downing KM. Parenting activities and the transition to home-based education during the COVID-19 pandemic. Child Youth Serv Rev 2021;122:105585. https://doi.org/10.1016/j.childyouth.2020.105585. · DOI
  53. Leung L, Wei R. More than just talk on the move: uses and gratifications of the cellular phone. J Mass Commun Q 2000;77(2):308-20. https://doi.org/10.1177/ 107769900007700206. · DOI
  54. Lichtenstein A, Rosenfeld LB. Uses and misuses of gratifications research: an explication of media functions. Commun Res 1983;10(1):97-109. https://doi. org/10.1177/009365083010001005. · DOI
  55. Liu C, Bano M, Zowghi D, Kearney M. Analysing user reviews of inquiry-based learning apps in science education. Comput Educ 2021;164:104119. https://doi. org/10.1016/j.compedu.2020.104119. · DOI
  56. Luo MM, Remus W. Uses and gratifications and acceptance of Web-based information services: an integrated model. Comput Hum Behav 2014;38:281-95. https://doi.org/10.1016/j.chb.2014.05.042. · DOI
  57. Luo MM, Chea S, Chen JS. Web-based information service adoption: a comparison of the motivational model and the uses and gratifications theory. Decis Supp Syst 2011;51(1):21-30. https://doi.org/10.1016/j.dss.2010.11.015. · DOI
  58. McCreery SP, Krugman DM. TV and the iPad: how the tablet is redefining the way we watch. J Broadcast Electronic Media 2015;59(4):620-39. https://doi.org/ 10.1080/08838151.2015.1093483. · DOI
  59. McDermott R. Internal and external validity. Cambridge handbook of experimental political science. 2011. p. 27-40.
  60. McMillan SJ, Hwang JS. Measures of perceived interactivity: an exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. J Advert 2002;31(3):29-42. https://doi.org/10.1080/ 00913367.2002.10673674. · DOI
  61. McNiven MD, Krugman D, Tinkham SF. The big picture for large-screen television viewing: for both programming and advertising, audiences are more attentive, more absorbed, and less critical. J Advert Res 2012;52(4):421-32. https://doi. org/10.2501/JAR-52-4-421-432. · DOI
  62. Mishra L, Gupta T, Shree A. Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. Int J Educ Res Open 2020;1:100012. https://doi.org/10.1016/j.ijedro.2020.100012. · DOI · Paper page
  63. Montazami A. Why this App? How educators and parents choose good educational apps. McGill University (Canada; 2020. Doctoral dissertation.
  64. Moro C, Smith J, Finch E. Improving stroke education with augmented reality: a randomised control trial. Comput Educ Open 2021;2:100032. https://doi.org/ 10.1016/j.caeo.2021.100032. · DOI
  65. Muthuprasad T, Aiswarya S, Aditya KS, Jha GK. Students' perception and preference for online education in India during COVID-19 pandemic. Soc Sci Humanit Open 2021;3(1):100101. https://doi.org/10.1016/j. ssaho.2020.100101. · DOI
  66. Nikhashemi SR, Knight HH, Nusair K, Liat CB. Augmented reality in smart retailing: a (n)(A) symmetric approach to continuous intention to use retail brands' mobile AR apps. J Retail Consum Serv 2021;60:102464. https://doi.org/ 10.1016/j.jretconser.2021.102464. · DOI · Paper page
  67. Nikken P, Schols M. How and why parents guide the media use of young children. J Child Fam Stud 2015;24(11):3423-35. https://doi.org/10.1007/s10826-015- 0144-4. · DOI · Paper page
  68. O'Shea PM, Elliott JB. Augmented reality in education: An exploration and analysis of currently available educational apps. In: International conference on immersive learning. Cham: Springer; 2016. p. 147-59. https://doi.org/10.1007/ 978-3-319-41769-1_12. · DOI
  69. Palmgreen P. Uses and gratifications: a theoretical perspective. Annal Int Commun Assoc 1984;8(1):20-55. https://doi.org/10.1080/ 23808985.1984.11678570. · DOI
  70. Papacharissi Z, Rubin AM. Predictors of Internet use. J Broadcast Electronic Media 2000;44(2):175-96. https://doi.org/10.1207/s15506878jobem4402_2. · DOI · Paper page
  71. Papadakis S, Kalogiannakis M, Zaranis N. Educational apps from the android google play for Greek preschoolers: a systematic review. Comput Educ 2018;116: 139-60. https://doi.org/10.1016/j.compedu.2017.09.007. · DOI
  72. Papadakis S, Vaiopoulou J, Kalogiannakis M, Stamovlasis D. Developing and exploring an evaluation tool for educational apps (ETEA) targeting kindergarten children. Sustainability 2020;12(10):4201. https://doi.org/10.3390/ su12104201. · DOI
  73. Ray A, Dhir A, Bala PK, Kaur P. Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J Retail Consum Serv 2019;51:221-30. https://doi.org/10.1016/j.jretconser.2019.05.025. · DOI · Paper page
  74. Rubin AM. Uses-and-gratifications perspective on media effects. Media effects. Routledge; 2009. p. 181-200.
  75. Saunders M, Lewis P, Thornhill A. Research methods for business students. 5th Ed. Pearson Education India; 2009.
  76. Schenke K, Redman EJ, Chung GK, Chang SM, Feng T, Parks CB, Roberts JD. Does "Measure Up!" measure up? Evaluation of an iPad app to teach preschoolers measurement concepts. Comput Educ 2020;146:103749. https://doi.org/ 10.1016/j.compedu.2019.103749. · DOI
  77. Shah NL, Miller JB, Bilal M, Shah B. Smartphone apps in graduate medical education virtual recruitment during the COVID-19 pandemic. J Med Syst 2021; 45(3):1-4. https://doi.org/10.1007/s10916-021-01720-z. · DOI
  78. Sheldon P, Bryant K. Instagram: motives for its use and relationship to narcissism and contextual age. Comput Hum Behav 2016;58:89-97. https://doi.org/ 10.1016/j.chb.2015.12.059. · DOI · Paper page
  79. Shroff RH, Keyes CJ. A proposed framework to understand the intrinsic motivation factors on university students' behavioral intention to use a mobile application for learning. J Inf Technol Educ 2017;16(1):143-68.
  80. Smith RA, Kim Y, Zhu X, Doudou DT, Sternberg ED, Thomas MB. Integrating models of diffusion and behavior to predict innovation adoption, maintenance, and social diffusion. J Health Commun 2018;23(3):264-71. https://doi.org/ 10.1080/10810730.2018.1434259. · DOI
  81. SSundar SS. The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger MJ, Flanagin AJ, editors. Digital media, youth, and credibility. Cambridge, MA: The MIT Press; 2008. p. 72-100. https://doi.org/ 10.1162/dmal.9780262562324.073. · DOI
  82. Sumter SR, Vandenbosch L, Ligtenberg L. Love me Tinder: untangling emerging adults' motivations for using the dating application Tinder. Telemat Inform 2017; 34(1):67-78. https://doi.org/10.1016/j.tele.2016.04.009. · DOI
  83. Sun B, Loh CE, Nie Y. The COVID-19 school closure effect on students' print and digital leisure reading. Comput Educ Open 2021;2:100033. https://doi.org/ 10.1016/j.caeo.2021.100033. · DOI
  84. Sundar SS. Social psychology of interactivity in human-website interaction. Oxford handbook of internet psychology. Oxford University Press; 2012. https:// doi.org/10.1093/oxfordhb/9780199561803.013.0007. · DOI
  85. Talwar S, Dhir A, Kaur P, Zafar N, Alrasheedy M. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. J Retail Consum Serv 2019;51:72-82. https://doi.org/10.1016/j. jretconser.2019.05.026. · DOI
  86. Talwar S, Dhir A, Singh D, Virk GS, Salo J. Sharing of fake news on social media: application of the honeycomb framework and the third-person effect hypothesis. J Retail Consum Serv 2020;57:102197. https://doi.org/10.1016/j. jretconser.2020.102197. · DOI
  87. Tang YM, Chen PC, Law KM, Wu CH, Lau YY, Guan J, Ho GTS. Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Comput Educ 2021;168: 104211. https://doi.org/10.1016/j.compedu.2021.104211. · DOI
  88. Teo T, Zhou M. Explaining the intention to use technology among university students: a structural equation modeling approach. J Comput High Educ 2014;26 (2):124-42. https://doi.org/10.1007/s12528-014-9080-3. · DOI
  89. Turner M, Kitchenham B, Brereton P, Charters S, Budgen D. Does the technology acceptance model predict actual use? A systematic literature review. Inf Softw Technol 2010;52(5):463-79. https://doi.org/10.1016/j.infsof.2009.11.005. · DOI · Paper page
  90. Wai ISH, Ng SSY, Chiu DK, Ho KK, Lo P. Exploring undergraduate students' usage pattern of mobile apps for education. J Libr Inf Sci 2018;50(1):34-47. https://doi. org/10.1177/0961000616662699. · DOI
  91. Weiser EB. Gender differences in Internet use patterns and Internet application preferences: a two-sample comparison. Cyberpsychol Behav 2000;3(2):167-78. https://doi.org/10.1089/109493100316012. · DOI
  92. Yuan S, Ma W, Kanthawala S, Peng W. Keep using my health apps: discover users' perception of health and fitness apps with the UTAUT2 model. Telemed e-Health 2015;21(9):735-41. https://doi.org/10.1089/tmj.2014.0148. · DOI
  93. Yusof AF, Iahad NA. The effect of perceived interactivity and social norm to the continuance use of mobile wellness apps. In: 2019 6th International conference on research and innovation in information systems (ICRIIS). IEEE; 2019. p. 1-7. https://doi.org/10.1109/ICRIIS48246.2019.9073344. · DOI
  94. Zhou G, Xu J. Adoption of educational technology: how does gender matter? Int J Teach Learn High Educ 2007;19(2):140-53.
  95. Ali Z, Ghazali MAIM, Hussin S, Ayub AFM, Kassim H, Muhammad N, Ismail R, Majid SNA. VocBlast: a preview of a mobile vocabulary application to learn specialised engineering vocabulary. In: Paper presented at International Seminar on Language Teaching; 2015. https://doi.org/10.13140/RG.2.1.4061.8008. · DOI
  96. Zydney JM, Warner Z. Mobile apps for science learning: review of research. Comput Educ 2016;94:1-17. https://doi.org/10.1016/j.compedu.2015.11.001. · DOI

Cited by

Sorted by citations

Authors
Menon, D.
Affiliations
Indian Space Research Organisation (IN)
Corresponding author
Menon, Devadas
Venue
Computers and Education Open
Publisher
Elsevier BV
Published
2022-12
Volume
3
Pages
100076
ISSN
2666-5573 (ISSN-L)
OpenAlex
W4221129254
Cited by
59
References
15
Citations by year
2026: 7 · 2025: 10 · 2024: 19 · 2023: 17 · 2022: 6
FWCI
12.87
Type
journal article
Language
en
Open access
Locations
  • Computers and Education Open (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Technology-Enhanced Education Studies
Keywords
educational apps, uses and gratifications theory, student motivation, intention to use, COVID-19 pandemic, academic support, convenience, social influence, novelty, engagement, gender differences, scale development

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review