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The purpose of this paper is to identify the success factors and challenges for students studying in an open and distance learning (ODL) mode and recommend strategies for student persistence based on the findings.

Three groups of ODL students with various levels of study performance – nine high-level, nine mid-level and eight low-level students – were invited to participate in three focus group interviews. They were asked about their motivation, success factors and challenges in their studies.

The different groups of participants showed observable variations in their response. The mid-level students believed that word-by-word rote memorisation was their best strategy in preparing for examinations. The low-level students believed that they needed to master multitasking to learn well in tight schedules. All these weak student participants considered quitting at some points, but no high-level student did so. To improve student persistence, the authors focus on meeting the needs of weak students and recommend the following actions for student persistence: add a time management and study skills component to existing courses for students to practise; appoint advisors to distance learning students to help them create an appropriate study plan and acquire a sense of belonging; make learning videos short and engaging; consider adopting student leaders or peer tutors that have been used successfully in full-time study; and conduct focus periodically with students to hear their views.

This study revealed the factors contributing to student persistence in ODL for the students of various levels of study performance. The results help in formulating measures to meet the diverse needs of ODL students for persistence in their studies.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.