Showcases/Positive Learning Experience
Business Problem

In recent years, the retention rate of universities in Australia was 89.5% on average, the average attrition figure was 10.5%. 89.5% of students stayed in courses, while 10.5% dropped out. 7.6% international students dropped out. In 32 universities, attrition rates ranged from 5.3% to 30.3%. Any loss of students therefore results in a loss of income for the institution. Many higher education institutions are still grappling with various strategies to improve their retention. Teaching and learning analytics enable teachers and teaching administrators to better understand student behaviours and the driving forces associated with their performance. It also aims to predict issues and provide best TL services, in order to tailor the best educational opportunities to each student's level of need, background and capability. Due to the lack of deep understanding of student learning behaviours and corresponding performance, existing methods show limited outcomes in improving learning performance and retention.
Our Solution

Our leading work in educational data mining results in deep understanding of student learning behaviours, behaviour changes, key driving factors associated with failure and drop-off cases, and contrast analysis of high-performing versus low-performing learning and teaching performance. Life-long learning and teaching data are involved in learning analytics and active student management, including the behaviour data of the learners, such as behaviours collected by online library, black board, access control, attendance book, academic activity log, preliminary trajectory, wifi access, and social media. Our model generates real-time risk score based on the student behaviours and presented to learners and lecturers through friendly interfaces. Key factors driving high academic risk are identified, with potential intervention strategies recommended to convert the learning behaviour and performance via our recommendation engine built on excellence of learning and teaching. Learner can access a mobile app to instantly observe performance against benchmarks, receive early warning and suggestions of intervening, the learning path and positive activities to be undertaken.
Deliverables & Benefits

  • Personalised Learning Plan (PLP) Personalized learning strategies are generated by the recommendation engine based on the individual performance and behaviour patterns. PLP creates the positive pathway that a student is recommended to follow to achieve the target, with a clear destination set for a student in terms of existing situations and circumstance. The system identifies the potential obstacles that might prevent or slow a student from getting to the goals. Benchmarked on most outstanding learning groups, the engine recommends the most suitable path for an individual.
  • Actionable recommendation Building on our theory of actionable knowledge discovery, factors driving failure/dropping out are mined. Furthermore, causal rules are generated to induce actionable strategies for both lecturer and students. By following the recommendation, the expected outcome could be better achieved.
  • High retention rate Recommending early intervention on those likely have trouble at the right time can substantially improve learning satisfaction and interest, the retention rate would thus be lifted significantly.