Tolerance of Characteristics and Attributes in Developing Student’s Academic Achievements

The purpose of this research is to study the relevance of factors for the analysis of the effectiveness of suitable educational institutions that illustrate the significance of the characteristics and attributes of the student's academic achievements and to identify the acceptance and tolerance of each attribute, which supports lifelong learning. The data used in this research is 1109 students who used and tested the institution recommender system based on student context and educational institution application. The research methodology focuses on the study of user involvement and application analysis. There are six significant phases of the research: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The machine learning tools and data mining techniques are k-means, k-medoids, decision trees, cross-validation methods, and confusion matrix. From the data analysis, it can be concluded that the overall level of satisfaction with the application is accepted (average = 3.70, S.D. = 0.84). In addition, the prototype model has been developed for predicting and recommending appropriate institutions for the learner has moderate accuracy levels (92.25%), and the results of the self-test data model are very accurate at the highest level, which is equal to 93.78%. Finally, this research demonstrates the relevance and success of education engineering projects. It demonstrates a worthy accomplishment. For future research, the researchers aim to construct and develop applications that promote and support the findings of this research.