Skip to main content

Evaluating an Adaptive Intervention in Collaboration Scripts Deconstructing Body Image Narratives in a Social Media Educational Platform

  • Conference paper
  • First Online:
Collaboration Technologies and Social Computing (CollabTech 2022)


Social Media is an important disseminator of body image representations and the body cult. The growing popularity of social media among children and adolescents makes minors a vulnerable group to the internalization of body ideals and stereotypes. Developing educational interventions that provide adolescents with skills to better understand the body image in social media is therefore necessary to counteract the effects of deceitful representations and discourse. This paper evaluates an adaptive educational intervention to define the suitable approach to teach adolescents about body image and stereotyping in social media. In particular, the paper examines and compares three approaches to identify the dominant body image stereotype in students’ social media: The self-reported methods, the analysis of social preferences, and the use of xAPI to track users’ behavior. Results showed that the use of xAPI combined with self-reported answers can provide better input from adolescents’ preferences. Moreover, it allows the automatic distribution of suitable counter-narratives to students participating in computer-supported collaborative learning activities embedded in an educational social media platform.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. de Lenne, O., Vandenbosch, L., Eggermont, S., Karsay, K., Trekels, J.: Picture-perfect lives on social media: a cross-national study on the role of media ideals in adolescent well-being. Media Psychol. 23(1), 52–78 (2020)

    Article  Google Scholar 

  2. Fardouly, J., Vartanian, L.R.: Social media and body image concerns: current research and future directions. Curr. Opin. Psychol. 9, 1–5 (2016).

    Article  Google Scholar 

  3. Ahadzadeh, A.S., Pahlevan Sharif, S., Ong, F.S.: Self-schema and self-discrepancy mediate the influence of Instagram usage on body image satisfaction among youth. Comput. Hum. Behav. 68, 8–16 (2017).

    Article  Google Scholar 

  4. Marengo, D., Longobardi, C., Fabris, M.A., Settanni, M.: Highly-visual social media and internalizing symptoms in adolescence: the mediating role of body image concerns (2018).

  5. Verrastro, V., Liga, F., et al.: Fear the Instagram: beauty stereotypes, body image and Instagram use in a sample of male and female adolescents. Qwerty Open Interdiscip. J. Technol. Cult. Educ. 15, 31–49 (2020).

  6. Cash, T.F., Smolak, L. (eds.): Body Image: A Handbook of Science, Practice, and Prevention. Guilford Press (2011)

    Google Scholar 

  7. Saiphoo, A.N., Vahedi, Z.: A meta-analytic review of the relationship between social media use and body image disturbance. Comput. Hum. Behav. 101, 259–275 (2019).

    Article  Google Scholar 

  8. Hou, Y., Xiong, D., Jiang, T., et al.: Social media addiction: its impact, mediation, and intervention. Cyberpsychol. J. Psychosoc. Res. Cyberspace (2019).

  9. McLean, S., Wertheim, E., Masters, J., Paxton, S.: A pilot evaluation of a social media literacy intervention to reduce risk factors for eating disorders. Int. J. Eat. Disord. 50, 847–851 (2017).

    Article  Google Scholar 

  10. Sánchez-Reina, J.R., Fuentes, C.B.: Comunicación De La Salud En La Campaña «Chécate, Mídete, Muévete». Representaciones y eficacia. Razón y Palabra 20(94), 645–662 (2016)

    Google Scholar 

  11. Hernández-Leo, D., Theophilou, E., Lobo, R., Sánchez-Reina, R., Ognibene, D.: Narrative scripts embedded in social media towards empowering digital and self-protection skills. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds.) EC-TEL 2021. LNCS, vol. 12884, pp. 394–398. Springer, Cham (2021).

    Chapter  Google Scholar 

  12. New Media Consortium: NMC Horizon Report: 2018 Education Edition. Retrieved June (2018)

    Google Scholar 

  13. Hattie, J.: Visible Learning: A Synthesis of over 800 Meta-analyses Relating to Achievement. Routledge, London (2008)

    Book  Google Scholar 

  14. Taylor, D.L., Yeung, M., Bashet, A.Z.: Personalized and adaptive learning. In: Ryoo, J., Winkelmann, K. (eds.) Innovative Learning Environments in STEM Higher Education. SpringerBriefs in Statistics, pp. 17–34. Springer, Cham (2021).

    Chapter  Google Scholar 

  15. Woolf, B.P.: Student modeling. Stud. Comput. Intell. 308, 267–279 (2010).

    Article  Google Scholar 

  16. Baiti, N.: Identification of personal traits in adaptive learning environment: systematic literature review. Comput. Educ. 130, 168–190 (2019). ISSN 0360-1315

  17. Dillenbourg, P.: Split where interaction should happen-a model for designing CSCL scripts. In: Instructional Design for Effective and Enjoyable Computer-Supported Learning, pp. i–ii (2004)

    Google Scholar 

  18. Jermann, P., Dillenbourg, P.: Elaborating new arguments through a CSCL script. In: Andriessen, J., Baker, M., Suthers, D. (eds.) Arguing to Learn, pp. 205–226. Springer, Dordrecht (2003).

    Chapter  Google Scholar 

  19. Amarasinghe, I., Hernández-Leo, D., Jonsson, A.: Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations. User Model. User-Adap. Inter. 29(4), 869–892 (2019).

    Article  Google Scholar 

  20. Fasihuddin, H., Skinner, G., Athauda, R.: Towards an adaptive model to personalise open learning environments using learning styles. In: Proceedings of International Conference on Information, Communication Technology and System (ICTS), pp. 183–188 (2014).

  21. Aslan, S., et al.: Students’ emotional self-labels for personalized models. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK 2017), pp. 550–551. Association for Computing Machinery, New York (2017).

  22. Hidayat, A., Utomo, V.G.: Automatic detection of learning style in adaptive online module system. In: 2016 International Conference on Informatics and Computing (ICIC), pp. 94–98 (2016).

  23. Verrastro, V., Fontanesi, L., Liga, F., Cuzzocrea, F., Gugliandolo, M.C.: Fear the Instagram: beauty stereotypes, body image and Instagram use in a sample of male and female adolescents. Qwerty 15(1), 31–49 (2020).

  24. Niemann, Y.F., Jennings, L., Rozelle, R.M., Baxter, J.C., Sullivan, E.: Use of free responses and cluster analysis to determine stereotypes of eight groups. Pers. Soc. Psychol. Bull. 20(4), 379–390 (1994).

    Article  Google Scholar 

  25. Butkowski, C.P., Dixon, T.L., Weeks, K.R., Smith, M.A.: Quantifying the feminine self(ie): gender display and social media feedback in young women’s Instagram selfies. New Media Soc. 22(5), 817–837 (2020).

    Article  Google Scholar 

  26. Kitto, K., Cross, S., Waters, Z., Lupton, M.: Learning analytics beyond the LMS, pp. 11–15 (2015).

  27. Cooper, A.: Learning analytics interoperability-the big picture in brief. Learn. Anal. Community Exchange 1–7 (2014)

    Google Scholar 

  28. De Croon, R., Wildemeersch, D., Wille, J., Verbert, K., Vanden Abeele, V.: Gamification and serious games in a healthcare informatics context. In: Proceedings of 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018, August, pp. 53–63 (2018).

  29. Wang, Y., Wang, M.: Data acquisition model for online learning activity in distance English teaching based on xAPI. Int. J. Continuing Eng. Educ. Life Long Learn. 31(1), 1–16 (2021)

    Article  Google Scholar 

  30. Manso-Vazquez, M., Caeiro-Rodriguez, M., Llamas-Nistal, M.: An xAPI application profile to monitor self-regulated learning strategies. IEEE Access 6, 42467–42481 (2018).

    Article  Google Scholar 

  31. Davies, G., Ouellet, M., Bouchard, M.: Toward a framework understanding of online programs for countering violent extremism. J. Deradicalization 6, 51–86 (2016)

    Google Scholar 

  32. Baldiris, S., Graf, S., Fabregat, R.: Dynamic user modeling and adaptation based on learning styles for supporting semi-automatic generation of IMS learning design. In: IEEE International Conference on Advanced Learning Technologies, pp. 218–220. IEEE Computer Society, July 2011

    Google Scholar 

  33. Lukasenko, R., Grundspenkis, J.: Adaptation of intelligent knowledge assessment system based on learner’s model. In: Proceeding on the 16th International Conference on Information and Software Technologies, Kaunas, Lithuania (2010)

    Google Scholar 

  34. Normadhi, N.B.A., Shuib, L., Nasir, H.N.M., Bimba, A., Idris, N., Balakrishnan, V.: Identification of personal traits in adaptive learning environment: systematic literature review. Comput. Educ. 130, 168–190 (2019)

    Article  Google Scholar 

Download references


This work has been partially supported by grants PID2020-112584RB-C33, MDM-2015-0502 funded by MICIN/AEI/ and the Volkswagen Stiftung (Courage, ref. 95 566). D. Hernández-Leo (Serra Húnter) acknowledges the support by ICREA under the ICREA Academia programme.

Author information

Authors and Affiliations


Corresponding author

Correspondence to René Lobo-Quintero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lobo-Quintero, R., Theophilou, E., Sánchez-Reina, R., Hernández-Leo, D. (2022). Evaluating an Adaptive Intervention in Collaboration Scripts Deconstructing Body Image Narratives in a Social Media Educational Platform. In: Wong, LH., Hayashi, Y., Collazos, C.A., Alvarez, C., Zurita, G., Baloian, N. (eds) Collaboration Technologies and Social Computing. CollabTech 2022. Lecture Notes in Computer Science, vol 13632. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20217-9

  • Online ISBN: 978-3-031-20218-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics