SLP Header

The Performance of First Aid Training e- Course among the Students of SCSVMV University

IJCSEC Front Page

The “First-Aid awareness e-course” provides the basic medical awareness to the users. This course covers emergency situations. The main objective is achieving the medical awareness, social conscious and a friendly society through First-Aid awareness e-course. Most of the E-learning course has limitations such as scarcity of content, lack of intelligent search and context sensitive personalization problems, which are challenging tasks for researcher to take up this problem. The main aim of the model developed is to get consistency in content delivery, quality content in learning materials, students self-learning concept, and performance improvement in their examination of awareness course. A study has been conducted to measure the performance of student’s awareness of First Aid training course among the students of SCSVMV University. The main aim of the model developed is to get consistency in content delivery, quality content in learning of first-aid awareness, students self-learning concept, and performance improvement in their performance.
E-Learning is the use of technology to enable people to learn anytime and anywhere. This type of learning can include training, the delivery of just-in-time information and guidance from experts. The “First-Aid awareness e-course” system helps to develop the emergency awareness skills and knowledge. Feedbacks are collected from the user through several questions to analyze about the system and user can send their suggestions too. E-Learning is the use of telecommunication technology to deliver information for education and training. With the progress of information and communication technology development, e-Learning is emerging as the paradigm of modern education. The great advantages of e-Learning include liberating interactions between learners and instructors, or learners and learners, from limitations of time and space through the asynchronous and synchronous learning network model (Katz, 2000; Katz, 2002; Trentin, 1997). E-learning’s characteristics fulfill the requirements for learning in a modern society and have created great demand for e-Learning from businesses and institutes of higher education. In the following sections, previous research, related literatures are discussed. A Model of e-learning system has developed. Finally, the results are analyzed and presented.
E-Learning is basically a web-based system that makes information or knowledge available to users or learners and disregards time restrictions or geographic proximity. Although E-learning of First-Aid awareness is use to avoid problems and make life be healthy. Many researchers from psychology and information system fields have identified important variables dealing with e-Learning. Among them, the technology acceptance model (Ajzen & Fishbein, 1977; Davis, Bagozzi, & Warshaw, 1989; Oliver, 1980), and the expectation and confirmation model (Bhattacherjee, 2001; Lin, Wu, & Tsai, 2005; Wu et al., 2006) have partially contributed to understanding e-Learning success. Table 1.1 shows the Comparative study of existing e-learning methods.


  1. Ajzen, I., & Fishbein, M. (1977). Attitude–behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918.
  2. Arbaugh, J. B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses – An exploratory study of two on-line MBA programs. Management Learning, 33(3), 331–347.
  3. Aronen, R., & Dieressen, G. (2001). Improvement equipment reliability through e-Learning. Hydrocarbon Processing, 47–57.
  4. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model. MIS Quarterly, 25(3), 270–351.
  5. Katz, Y. J. (2000). The comparative suitability of three ICT distance learning methodologies for college level instruction. Educational Media International, 37(1), 25– 30.
  6. Katz, Y. J. (2002). Attitudes affecting college students’ preferences for distance learning. Journal of Computer Assisted Learning, 18,2–9
  7. Lewis, C. (2002). Driving factors for e-Learning: an organizational perspective. Perspectives, 6(2), 50–54. Lin, Cathy S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42, 683–693.
  8. Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: a research framework and a preliminary assessment of effectiveness in basic IT skill training. MIS Quarterly, 25(4), 401–426.
  9. Wu, J. P., Tsai, R. J., Chen, C. C., & Wu, Y. C. (2006). An integrative model to predict the continuance use of electronic learning systems: hints for teaching. International Journal on E-Learning, 5(2), 287–302.
  10. Khairil Imran Ghauth and, Nor Aniza Abdullah, (2009) An Empirical Evaluation Of Learner Performance In E-Learning Recommender Systems And An Adaptive Hypermedia System, pp 141-152.
  11. Jonassen, D. H., Computers in the Classroom, Englewood Cliffs, NJ:Merrill, Keefe, J. W. (1987), in "Learning Style”.
  12. Peters, J., Jarvis, P. et al., Adult Education, San Francisco, CA, Ed Rogers, A., Teaching Adults, Buckingham: OU Press
  13. Jemni, M., & Nasraoui, O. (2009). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology & Society, 12(4), 30–42.
  14. Liang, G., Weining, K. & Junzhou, L. (2006). Courseware recommendation in e-learning system. Advances in Web Based Learning – ICWL2006, Springer Berlin/Heidelberg, 10-24.
  15. Kerkiri, T., Manitsaris, A. & Mavridou, A. (2007). Reputation metadata for e commending personalized e-learning resources. Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization, Uxbridge, 110-115.