Evaluating the use of Mock Examination as a Predictor of Primary School Students Performance in High Stakes Examinations
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Abstract
Learning analytics is emerging as a crucial discourse which can be useful in predicting students’ academic performance and in qualitative development of learning to redress educational challenges. However, primary school teachers in Botswana seem impervious to the significance of the vast amount of data generated on daily transactions between themselves and students, and/or between students and content. While keeping in mind the enormous quantity of educational data that could result from interactive relationships in the classroom, I sought to assess the predictability of Primary School Leaving Examinations (PSLE) results using mock examinations. A mixed method research employing correlational design underpinned the approach of the study. Secondary data were collected (N = 152) and analyzed using Binary Logistic Regression. Results showed that mock examination scores are a significant predictor of academic success. A logit regression algorithm was derived. Findings provided an alternative strategy to foretell the academic achievement of students. Implications on assessment practices and on teaching and learning are discussed.
Keywords
Assessment, Botswana, Logistic regression, Mock examination, Prediction, Primary school leaving examinations
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