Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists

Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists

© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Tipparat Sittiwong, Wongpanya S. Nuankaew, Patchara Nasa-Ngium, Pratya Nuankaew
DOI : 10.14445/22315381/IJETT-V70I11P235

How to Cite?

Tipparat Sittiwong, Wongpanya S. Nuankaew, Patchara Nasa-Ngium, Pratya Nuankaew, "Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 324-337, 2022. Crossref,

This research investigated the insight analysis of learning patterns that affect graduates' employment prospects. There are three objectives: 1) to study the context and learning patterns that encourage learners' achievement through educational data mining techniques, 2) to develop the forecasting models and construct an application for forecasting appropriate academic achievement clusters, and 3) to assess the application for forecasting the appropriate academic achievement clusters. Samples were collected from 227 students at the Department of Educational Technology and Communication, the Faculty of Education, Naresuan University, during the academic year 2015 – 2020. Research instruments were divided into two categories: statistical and data mining. The results showed that the context of the students in the curriculum offered a high level of academic performance distributed at high levels of academic achievement, which was 3.58 of an average overall student GPA. In addition, the developed forecasting model provided a high level of accuracy, which was 71.11 percent accuracy. At the same time, the level of satisfaction with the application was the highest level of satisfaction, which was 4.21 of the overall satisfaction with 0.66 of S.D. It can be concluded that this research is valuable and beneficial to educational technologists, who are driving the adoption of technology in education.

Insight Analysis, Learning Patterns, Educational Data Mining, Forecasting Model.

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