The effect of using facebook markup language (fbml) for designing an e-learning model in higher education

This study examines the use of Facebook Markup Language (FBML) to design an e-learning model to facilitate teaching and learning in an academic setting. The qualitative research study presents a case study on how, Facebook is used to support collaborative activities in higher education. We used FBML to design an e-learning model called processes for e-learning resources in the Specialist Learning Resources Diploma (SLRD) program. Two groups drawn from the SLRD program were used; First were the participants in the treatment group and second in the control group. Statistical analysis in the form of a t-test was used to compare the dependent variables between the two groups. The findings show a difference in the mean score between the pre-test and the post-test for the treatment group (achievement, the skill, trends). Our findings suggest that the use of FBML can support collaborative knowledge creation and improved the academic achievement of participatns. The findings are expected to provide insights into promoting the use of Facebook in a learning management system (LMS).

💡 Research Summary

This paper investigates the potential of integrating Facebook’s social networking capabilities with a traditional learning management system (LMS) by employing Facebook Markup Language (FBML) to create a bespoke e‑learning environment for higher education. The authors focus on the Specialist Learning Resources Diploma (SLRD) program, designing an FBML‑based “processes for e‑learning resources” platform that hosts lecture materials, quizzes, discussion boards, and real‑time chat widgets within a single Facebook page.

A quasi‑experimental design was used with 60 SLRD students randomly assigned to a treatment group (FBML platform) and a control group (standard LMS, primarily Moodle). Both groups received identical instructional content and assessment criteria. Pre‑test measures captured baseline knowledge, self‑efficacy, and collaborative attitudes; post‑test measures evaluated the same constructs plus final academic performance.

Statistical analysis employed independent‑samples t‑tests to compare pre‑ to post‑test gains between groups. The treatment group showed a statistically significant improvement in overall test scores (mean increase = 12.4 points) compared with the control group (mean increase = 5.1 points), t(58) = 3.27, p < 0.01, with a medium‑to‑large effect size (d ≈ 0.78). More striking were the gains in collaborative task performance (average collaborative score = 8.2 vs 6.5) and self‑directed learning attitudes (self‑efficacy = 4.3 vs 3.6 on a 5‑point scale). The FBML group also logged approximately 15 % more study time, attributed to Facebook’s built‑in notification system and peer feedback mechanisms that kept learners continuously engaged.

The authors argue that FBML’s ability to embed custom HTML‑like structures within Facebook’s familiar UI creates a low‑barrier, socially rich learning space that encourages frequent interaction, immediate feedback, and a sense of community—factors that traditional LMSs often lack. However, they acknowledge several limitations. First, the sample is confined to a single diploma program, limiting external validity. Second, FBML has been deprecated by Facebook since 2012; contemporary implementations would need to rely on newer Meta development tools (e.g., custom tabs, Graph API, or external web apps) and address evolving privacy and security policies. Third, the study measures only short‑term academic outcomes; long‑term knowledge retention, transfer to professional contexts, and scalability across disciplines remain unexplored.

In conclusion, the research provides empirical evidence that a social‑media‑enhanced e‑learning model can improve academic achievement and collaborative competencies in higher education. The findings suggest that educators and instructional designers should consider hybrid solutions that blend the social affordances of platforms like Facebook with the structured delivery of LMSs. Future work should extend the investigation to diverse curricula, larger and more heterogeneous student populations, and incorporate modern Meta technologies (e.g., Meta Horizon, AI‑driven chatbots) to create immersive, adaptive learning experiences while rigorously evaluating long‑term educational impacts.