Mining the patterns of graduate students' self-regulated learning behaviors in a negotiated online academic reading assessment

Online academic reading assessments can test graduate students' reading ability, while the results of tests as a feedback can help students reflect on their own ability, realize their weaknesses, and then take actions to improve their ability. These behaviors form the basis of self-regulated learning ability. In this paper, a negotiated online reading assessment system is developed to provide students with the function of self-assessment and reflection. For designing the system, the authors use the concept of a negotiated learner model, so that students may negotiate with the system and try to reach an agreement. In order to explore the differences in students' learning behaviors in such a system, the authors used the hidden Markov Model to construct the behavioral model of students' self-regulated learning ability. Furthermore, the authors differentiated the negotiation behaviors of students with high and low self-regulated learning ability in the reading assessment system. The results showed that the students in the high and low self-regulated learning ability groups shared the behaviors of peer comparison and retesting. The high selfregulated learning group tended to transit among retesting, peer comparison, test reflection, and self-assessment. The students in the low self-regulated learning group tended to transit among retesting, peer comparison, viewing grades, and self-reflection. The results indicated that the students tended to re-test to negotiate with the system, and that the students in the high self-regulated learning group may reflect on learning through negotiation and further plan their learning strategies.