Outcomes-based student performance diagnostic and support model

The assessment of students' knowledge by evaluating individual course learning outcomes (CLOs) will assist in providing students with targeted assistance. This will also assist in providing assistance to struggling students as early as possible in their course learning outcomes. Furthermore, such an assessment offers lecturers an opportunity to offer personalized assistance to students. This paper proposes an Outcomes-based Student Performance Diagnostic and Support (OSPDS) model aimed at achieving the abovementioned goals, amongst other things. The model is aimed at aligning CLOs with the assessment criteria (AC) as well as assessments (AS) for a given topic, determining student's performance based on set CLOs, as well as proposing teaching and learning interventions for struggling students. As a form of evaluating the proposed model, a prototype was developed based on the OSPDS model. The sample test results obtained from the prototype indicate that the OSPDS model does fulfil its set goals.

[1]  V. Shanmugarajeshwari,et al.  Analysis of students' performance evaluation using classification techniques , 2016, 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16).

[2]  Pooja Thakar,et al.  Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue , 2015, ArXiv.

[3]  Gautam Biswas,et al.  Early Prediction of Student Dropout and Performance in MOOCs using Higher Granularity Temporal Information , 2014, J. Learn. Anal..

[4]  A. Shanableh.  IT-facilitated student assessment: Outcome-based student grades , 2011, 2011 International Conference on Information Technology Based Higher Education and Training.

[5]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[6]  Yma Pinto.  A strategy, implementation and results of a flexible competency based curriculum , 2010, INROADS.

[7]  Norman Poh,et al.  To what extend can we predict students' performance? A case study in colleges in South Africa , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[8]  Norshahriah Wahab,et al.  Predicting NDUM Student's Academic Performance Using Data Mining Techniques , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[9]  Albrecht Fortenbacher,et al.  Predicting students' success based on forum activities in MOOCs , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[10]  Z. Hari,et al.  Evaluating engineering students performance in physics courses during foundation studies , 2016, 2016 IEEE 8th International Conference on Engineering Education (ICEED).

[11]  Venky Shankararaman,et al.  Measuring student performance and providing feedback using Competency Framework , 2014, 2014 IEEE 6th Conference on Engineering Education (ICEED).

[12]  R Lakshmana Kumar,et al.  Research contemplate on educational data mining , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).