TY  - JOUR
T1  - Application of Context-Aware Business Intelligence Framework in Determining Riskiness of Academic Modules Within Tertiary Institutions
AU - Kadyamatimba, Armstrong AU - Mutanga, Alfred AU - Mavetera, Nehemaih 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 11
SP  - 801
EP  - 809
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.801.809
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.801.809
KW  - Risk modules
KW  -business intelligence
KW  -degree of difficulty
KW  -content knowledge
KW  -strong
KW  -institution
AB  - <p>This study shows the researchers efforts to determine the riskiness of academic modules at a South African University. In determining the modules at risk, the researchers used a hybrid of sequential and cyclical methodological approaches based on a context-aware business intelligence framework. The risk indicators derived from academic module enrolment data elements were weighted and aggregated to determine the riskiness of a module. The results showed that the riskiness of a module can be zero, weak, strong and extreme. Depending on the riskiness of a module, the institution can determine the appropriate intervention strategies for students to succeed in passing the module. This research proves to be essential for program and module reviews which are important quality assurance exercises within the South African higher education institutions.</p>

ER  -