This paper investigates whether Foreign Direct Investment (FDI) crowds-out domestic investment in Uganda. We analyse the effect on the aggregate economy and at sectoral level using data from 1992 to 2012. We obtain a robust neutral effect on the overall economy. At sector level, we find a crowding-out effect in four sectors; a crowding-in effect in two sectors and a neutral effect in three sectors. But generally, results are robust in only six sectors. Finally, an exogeneity test reveals that past economic growth rates do not influence the inflow of FDI, hence there is no endogeneity problem in our analysis.

Purpose The purpose of this paper is to propose a theoretical model that integrates various dimensional factors which influence decision-making process of class selection and enrolment, analysing different angles of this process and explaining those factors which determine students’ decision. Design/methodology/approach This study uses quantitative design to determine and explore students’ decision making in class selection and enrolment. There were 396 students who participated in this study. The data were analysed using principle component analysis to determine the dominant factor for class selection and enrolment. Findings The study has analysed different factors that can influence students’ decision for class selection and enrolment. Five important underlying factors have been identified which includes the class and lecturer factor, time-space factor, ease and comfort factor, course mate factor and commitment factor. Moreover, the Kruskal–Wallis test shows that there is a significant mean difference in choice and selection behaviour between genders and students’ personal attitudes. Research limitations/implications This study is an early attempt to explore the wide fields of decision making in class selection and enrolment. It is hoped that follow-up studies would provide more coverage relative to the findings of this research. Practical implications One particular dimension of micro decision making faced by students is class (course) selection in the beginning of every academic semester/term. Class selection is very critical decision for students as it would reflect students expected outcome for their future career directions. More importantly, the decision made by the students may also affect their academic performance throughout their study. Social implications From the perspective of the university’s administrators, this issue is very critical for planning purposes. Understanding the students’ behaviour in class selection could improve the cost effectiveness as well as the scheduling of course offering to enhance students’ and instructors’ teaching and learning experience. Originality/value While many studies try to explore the questions of what makes a student choose a specific college/university or a specific field, limited number have investigated the behaviour of students in class selection and enrolment. This paper contributes to bridging that gap.

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Data pre-processing is a crucial phase prior to analytic task and yet rarely been discussed especially for e-learning data which has multilevel data. Providing a reliable data pre-processing is important to provide quality dataset. Therefore, this study investigates the problems arise in data pre-processing and in this case, for identifying the implement prediction task. A MOOC dataset is selected for the data pre process in generating the summary of dataset is explained and the ultimate aim is to produce a dataset with features that are ready for data model and suggestions, which can be applied to support more comprehensible tools for educational domain who is the end user. Subsequently, the data pre efficient for predicting student's