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The integration of information communication and technology in medical education has led to the emergence of modern instructional tools, thus transforming pedagogy. Modern instructional tools like 3D animation are very effective tools for pedagogy, especially in subjects that involve complex concepts that are difficult to represent with traditional instructional tools such as textbooks, and lectures. The main objective of this study was to determine the effectiveness of using 3D animation in teaching psychiatry to clinical medicine students at the Kenya Medical Training College. The study used a randomized comparative design, where participants were randomly assigned to either a treatment group (3D animation) or a control group (textbook). Data were collected using self-administered questionnaires. The study targeted 61 respondents, and the sample size was 53 respondents (11 lecturers and 42 students). A pilot test to ensure the reliability and validity of the questionnaires was done on 30 respondents from the nursing department at Kenya Medical Training College. The analysis of the results was done using the Statistical Package for Social Sciences software version 29, whereby descriptive statistics were used to describe feedback from the lecturers and students while independent sample t-tests were used to determine the statistically significant difference in student scores. The t-test results showed a statistically significant difference in means between the two groups, with t (40) = -7.332, p < 0.001 (two-tailed). The mean difference between the two groups was -8.190, with a standard error of 1.117. The 95% confidence interval of the difference ranged from -10.448 to -5.933. Therefore, the use of 3D animation had a statistically significant positive effect on test scores compared to textbook instructional materials in the context of teaching psychiatry. The study recommends the integration of 3D animations in the medical education curriculum at Kenya Medical Training College and other institutions of higher learning.&nbsp

Stakeholders in Kenyan education are concerned about student performance. Data mining has emerged as an alternate method for education stakeholders to employ in making decisions about student performance in their final year exam. Kenya's education sector provides a wealth of statistical data that might provide vital information about students. Information and communication technology collects and compiles low-cost data that can be used to forecast student performance. However, no meaningful information is extracted from this data by Kenyan educational institutions. In this paper, we propose and develop a prediction model for forecasting Kenya secondary school learner performance utilizing prior performance data from students, which will be transformed and cleaned before being used in training and testing the model. Our model employs data mining techniques to improve forecast accuracy. We will present the model theoretical framework, conceptual framework, and outcomes.