Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics

被引:19
作者
Katz, Sandra [1 ]
Albacete, Patricia [1 ]
Chounta, Irene-Angelica [2 ]
Jordan, Pamela [1 ]
McLaren, Bruce M. [3 ]
Zapata-Rivera, Diego [4 ]
机构
[1] Univ Pittsburgh, Learning Res & Dev Ctr, Pittsburgh, PA 15260 USA
[2] Univ Tartu, Ctr Educ Technol, Tartu, Estonia
[3] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15213 USA
[4] Educ Testing Serv, Princeton, NJ 08541 USA
关键词
Tutorial dialogue systems; Adaptive instruction; Scaffolding; Physics education; OPEN LEARNER MODEL; SMALL-GROUP WORK; SUPPORT; KNOWLEDGE; CLASSROOM; EXAMPLES; INSTRUCTION; NEGOTIATION; EFFICIENCY; AUTOTUTOR;
D O I
10.1007/s40593-020-00226-y
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However, considerably less attention has been paid to using a student model to personalize instruction in tutorial dialogue systems (TDSs)-ITSs that engage students in natural-language, conceptual discussions. This paper describes Rimac, a TDS that tightly couples student modelling with tutorial dialogues about conceptual physics. Rimac is distinct from other TDSs insofar as it dynamically builds a persistent student model that guides reactive and proactive decision making in order to provide adaptive instruction. An initial pilot study set in high school physics classrooms compared a control version of Rimac without a student model with an experimental version that implemented a "poor man's student model"; that is, the model was initialized using students' pretest scores but not updated further. Both low and high prior knowledge students showed significant pretest to posttest learning gains. However, high prior knowledge students who used the experimental version of Rimac learned more efficiently than high prior knowledge students who used the control version. Specifically, high prior knowledge students who used the student model driven tutor took less time to complete the intervention but learned a similar amount as students who used the control version. A subsequent study found that both high and low prior knowledge students learned more efficiently from a version of the tutor that dynamically updates its student model during dialogues than from a control version that included the static "poor man's student model." We discuss future work needed to improve the performance of Rimac's student model and to integrate TDSs in the classroom.
引用
收藏
页码:397 / 445
页数:49
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