TY - GEN
T1 - Mining the patterns of graduate students' self-regulated learning behaviors in a negotiated online academic reading assessment
AU - Zhang, Xiaotong
AU - Cheng, Hercy N.H.
N1 - Funding Information:
This study was financially supported by the Fundamental Research Funds for the Central Universities (CCNU19QN034).
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
KW - Hidden Markov model
KW - Negotiated learner models
KW - Online reading assessment
KW - Self-regulated learning
UR - http://www.scopus.com/inward/record.url?scp=85073684462&partnerID=8YFLogxK
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U2 - 10.1145/3355966.3355977
DO - 10.1145/3355966.3355977
M3 - Conference contribution
AN - SCOPUS:85073684462
T3 - ACM International Conference Proceeding Series
SP - 109
EP - 114
BT - Proceedings of 2019 the 3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019
PB - Association for Computing Machinery, Inc
T2 - 3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019
Y2 - 15 August 2019 through 17 August 2019
ER -