Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School
Abstract
Today's technology has improved to the point that it can be utilized to execute many activities in daily life with minimum effort, and the world has acknowledged the worth of education in one's life. The schools have to analyze student performance manually, which requires a lot of time and effort from teachers to work on. However, the increasing amount of student data becomes difficult to analyze using traditional statistical techniques and database data management tools. The objective of this project is to study the current problems in the online student performance system. A preliminary survey of 30 respondents was conducted in order to gather information based on previous user experiences with the online student performance system. The next objective is to develop an Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School using Web Development Life Cycle that can visualize student performance systems to assist teachers and parents. Following that, this project employed a tool based on Multidimensional Data Visualization techniques. Google Charts and Dialogflow were used in this project to visualize the dashboard and construct a chatbot for the system. The last objective is to evaluate the usability of the system. There are three experts to test the project usability using the Post-Study System Usability Questionnaire (PSSUQ). The findings of the project can be used as a guideline to improve the system in the future. Overall, this project will assist teachers and parents in obtaining information about their students’ academic performance. The data about the students' performance can be displayed in the dashboard as a chart, graph, or diagram, and they can also communicate with the chatbot if they require assistance or guidance in using the system and obtaining their students' performance.
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References
S. Goulas and R. Megalokonomou, “School attendance during a pandemic,” Economics Letters, pp. 193, 2020. [Online] Available at https: //doi.org/10.1016/j.econlet.2020.109275 [Accessed: Jan. 23, 2022].
M. J. Pastizzo, R. F. Erbacher and L. B. Feldman, “Multidimensional data visualization,” Behavior Research Methods, Instruments, and Computers, vol. 34, no. 2, pp. 158-162, 2020.
E. T. Ampofo and B. O. Owusu, “Academic Ambition and Effort in the Public Senior High Schools,” International Journal of Academic Research and Reflection, vol. 3, no. 5, pp. 19–35, 2015.
A. Heryandi, “Developing Chatbot for Academic Record Monitoring in Higher Education Institution,” IOP Conference Series: Materials Science and Engineering, vol. 879, no. 1, 2020.
C. L. Sa, D. H. B. Abang Ibrahim, H. Dahliana, “Student performance analysis system (SPAS). 2014 the 5th International Conference on Information and Communication Technology for the Muslim World,” ICT4M 2014.
A. M. Nortvig, A. K. Petersen, and S. H. Balle, “A Literature Review of the Factors Influencing E-Learning and Blended Learning in Relation to Learning Outcome, Student Satisfaction and Engagement,” Electronic Journal of E-Learning, vol. 16, no. 1, pp. 46–55. 2018.
L. Lito, D. Mallillin, “Different Domains in Learning and the Academic Performance of the Students,” Journal of Educational System, vol.4, no. 1, pp. 1–11, 2020.
S. Bali and M. C. Liu, “Students perceptions toward online learning and face-to-face learning courses,” Journal of Physics: Conference Series, vol. 1108, no. 1, 2018.
F. Misra and I. Mazelfi, “Long-Distance Online Learning during Pandemic: The Role of Communication, Working in Group, and Self-Directed Learning in DevelopingStudent’s Confidence,” Atlantis Press, vol. 506, pp. 225–234, 2021.
R. Etom, J. Pabatang, K. Dapanas, R. Consolacion, J. Iniego, A. Jumao-as, A. Pabua, A and K. Tee, “The use of elearning tools in blended learning approach on students’ engagement and performance,” Journal of Physics: Conference Series, vol. 1835, no. 1, 2021.
M. I. Baig, L. Shuib and E. Yadegaridehkordi, “Big data in education: a state of the art, limitations, and future research directions,” In International Journal of Educational Technology in Higher Education, vol.17, no. 1, pp. 1–23, 2020.
N. Bikakis, “Big Data Visualization Tools. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies,” Springer, Cham. [Online] Available:https: //doi.org/10.1007/978-3-319-77525-8_109. [Accessed: February. 23, 2022].
M. Golfarelli, and S. Rizzi, “A model-driven approach to automate data visualization in big data analytics,” Information Visualization, vol. 19, no. 1, pp. 24–47.
Q. Li, “Overview of Data Visualization,” In Embodying Data, pp. 17–47, 2020.
N. Haristiani, “Artificial Intelligence (AI) Chatbot as Language Learning Medium: An inquiry. Journal of Physics: Conference Series, vol. 1387, no. 1. 2019.
O. Zawacki, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–27, 2019.
R. Farrahi, F. Rangraz, E. Nabovati, M. Sadeqi and R. Khajouei, “The relationship between user interface problems of an admission, discharge and transfer module and usability features: A usability testing method,” BMC Medical Informatics and Decision Making, vol.19, no. 1, pp. 1–8.
W. Holmes, M. Bialik and C. Fadel, “Artificial Intelligence in Education Promises and Implications for Teaching and Learning,” [Online] Available: http: //bit.ly/AIED- [Accessed: Feb. 23, 2022].
J. Borenstein, and A. Howard, “Emerging challenges in AI and the need for AI ethics education,” AI and Ethics, vol. 1, no. 1, pp. 61–65.
Z. Baharum, A. Amran, B. A. Kamsul, N. A. Ahmad and N. H. Azmi, “Data visualization for distribution of people with disabilities,” Journal of Physics: Conference Series, vol. 1860, no. 1, 2021.
K. Börner, A. Bueckle, and M. Ginda, “Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments,” Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 6, pp. 1857–1864, 2019.
A. Sarkar, “Overview of Web Development Life cycle in Software Engineering,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3, no. 6, 2018.