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Ramaswami and Bhaskaran (2010) constructed a prediction model called CHIAD to predict the performance in higher secondary school education. They collected the input data for this model from regular students, schools and chief educational officers of different district schools, with a total of 772 student records. They found numerous factors affecting the students’ performance, like: “medium of instruction, marks obtained in secondary education, location of school, living area and type of secondary education”.
Al-Radaideh et al. (2011) provided a classification approach (data mining technique) to guide students in the basic education stage in selecting their academic tracks. They developed a decision tree classification, then they extracted a set of rules with an overall accuracy of 87.9%.
Win and Miller (2005) employed two methodologies to determine the factors that influence university students’ academic performance (analogous to an input-output approach and random coefficients’ model) by using data of first year students at the University of Western Australia in 2001. They found that high school is the most affecting factor of the academic performance of the students at the university. Further, immersion and reinforcement were affecting the performance. Their results showed that there is a strong positive relationship between the first-year mark and the Tertiary Entrance Rank (TER). It was also shown that the type of school (governmental schools or otherwise) has an effect on TER.
Chamorro-Premuzic and Furnham (2003) studied students’ neuroticism, psychotics and conscientiousness, using them as factors that affect the students’ academic performance in: exams, final-year projects, student absenteeism and essay writing. They found that neuroticism weakens academic performance, whereas conscientiousness strengthens it. Also, they found that psychotics limit academic performance. Then, they provided evidence to supporting good personality measures in academic selection procedures.