Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
by Mvurya Mgala Supervisors: Dr Audrey Mbogho and Prof Gary Marsden PowerPoint Presentation
Download Presentation
by Mvurya Mgala Supervisors: Dr Audrey Mbogho and Prof Gary Marsden

by Mvurya Mgala Supervisors: Dr Audrey Mbogho and Prof Gary Marsden

152 Vues Download Presentation
Télécharger la présentation

by Mvurya Mgala Supervisors: Dr Audrey Mbogho and Prof Gary Marsden

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Investigating mobile based prediction modelling of academic performance for primary school pupils: a data mining approach. by MvuryaMgala Supervisors: Dr Audrey Mbogho and Prof Gary Marsden

  2. Introduction Problem statement • Low academic performance of primary school pupils in some regions has presented a worrying trend. • Research has shown this to be a widespread problem among developing nations. • The problem has been attributed to many factors, ranging from students’ personal factors, teacher factors, school factors, to family background factors.

  3. Motivation • The study will reveal the impact of the factors surrounding pupils’ low academic performance. • Discover the causes with the highest impact which can be used to build a prediction model. • The model will be used by education stakeholders to predict pupil’s performance. • Will propose intervention measures and facilitate informed decision making.

  4. Area of research Population: 649,931 Divisions: Kinango, Matuga, Msambweni, Kubo. Area: 832,200 ha

  5. What a contrast

  6. Crowded classes

  7. Lack of facilities

  8. Poverty

  9. So what is the impact of these factors on academic performance in primary schools in the developing world?

  10. Research Questions • How can the Bayesian classifier be modelled from the primary schools’ data? • How can a Bayesian model be used for prediction of primary school pupils’ academic performance? • What mobile application artefact can be designed to automatically predict academic performance?

  11. Significance of the study Findings of the study will contribute to the field of computer science and KDD in the following way: • Provide a process to design and create a prediction model artefact that predicts academic performance for primary school pupils. • Expose the social and technological issues that influence the successful design, implementation and adoption of an academic performance prediction model. • Support and enrich the classification approaches in implementation and adoption of prediction systems.

  12. Research Approach • Gather data through semi-structured interviews, questionnaires and secondary data, • Pre-process the data to extract relevant factors that affect academic performance • Data mining: apply specific algorithms to extract patterns from data, • Interpretation: making sense out of the extracted patterns, • Knowledge: the sense made out of the patterns,

  13. Data mining process Interpretation Data Mining Transformation Knowledge will be extracted by stakeholders from a mobile phone Preprocessing Selection Patterns Transformed Data Preprocessed Data Target Data Original Data Data from semi-structured interviews Questionnaires and secondary data

  14. Classification Algorithms “What factors determine low academic performance in primary schools?” General patterns Data Mining Algorithm Pupil, teacher, School, parent data Patterns coded into a mobile app Bayesian networks Classification Algorithm

  15. Methodology Target Population • The research will be targeted towards primary school pupils of 10 primary schools in Kwale County in Kenya. Sampling Design • Stratified sampling will be used since the target group is known. • A list of the factors will be obtained through literature and semi-structured interviews with 18 education officers. • Questionnaires will be given to 50 teachers and 200 pupils.

  16. Model evaluation-Confusion Matrix • Rows show the actual classes • Columns show the predicted classes

  17. Mobile artefact evaluation • The mobile application artefact will be evaluated using a field-based usability evaluation methodology

  18. Impact evaluation • Five pupils in their final year will randomly selected. • The prediction artefact will be used to determine their likely outcome in the final examination. • Some intervention measures will be put in place and the pupils’ final results compared with the predicted grades. • The study will therefore be able to propose possible interventions to the stakeholders.

  19. Contribution to Knowledge • Design and testing of a prediction model for academic performance of primary school pupils. • Provides an alternative to dependence on final examinations to determine students’ abilities • Provide means by which decision makers can make accurate decisions and effective policies.

  20. The end!