January 2012 Volume 15 Number 1 - Educational Technology ...

Wu, L., & Looi, C.-K. (2012). Agent Prompts: Scaffolding for Productive Reflection in an Intelligent Learning Environment.
Educational Technology & Society, 15 (1), 339–353.
Agent Prompts: Scaffolding for Productive Reflection in an Intelligent
Learning Environment
Longkai Wu and Chee-Kit Looi
National Institue of Education, Nanyang Technolgoical University, Singapore // longkai.wu@gmail.com //
cheekit.looi@nie.edu.sg
ABSTRACT
Recent research has emphasized the importance of reflection for students in intelligent learning environments.
This study tries to investigate whether agent prompts, acting as scaffolding, can promote students’ reflection
when they act as tutor through teaching the agent tutee in a learning-by-teaching environment. Two types of
agent prompts are contrasted in this research, both from the perspective of a tutee, and they differ in their
specificity. Generic prompts are aiming at eliciting students’ double-loop reflection on metacognitive strategies
and beliefs. Specific prompts, on the other hand, are to encourage students’ single-loop reflection on domainrelated
and task-specific skills and articulation of their explanatory responses. Our findings suggest that
designers of intelligent learning environment should concentrate on fostering students to reflect on their
metacognitive strategies and beliefs, and allow students to take responsibility for directing their own learning
autonomy.
Keywords
Question Prompts, Scaffolding, Learning-by-Teaching, Double-Loop Learning Theory
Introduction
Reflection, which is perceived as an active process of learning through experience (Dewey, 1910/1933), has been
prominent in educational literature (e.g., Boud, Keogh, & Walker, 1985; Kolb, 1984; Moon, 1999). Kemmis (1985)
suggests that reflection is a “dialectical process” that looks both inward at the individual’s thoughts and outwards at
the situation and is a “meta-thinking” process where the relationship between thought and action in a particular
context must be considered before moving to further thought and action. The ability to reflect has been associated
with the higher levels of learning in a number of taxonomies of learning objectives (Biggs & Collis, 1982; Bloom,
1956). Moon (1999) considers the ability to carry out meaningful reflection as indicative of the highest level of deep
learning. Chi, Siler, Jeong, Yamauchi and Hausmann (2001) have evidenced that only the number of student turns
coded as “reflection”, which were comprehension-monitoring statements, is positively correlated with deep learning
gains in the face-to-face (FTF) tutorial dialogues.
Question prompts, as suggested by previous studies (Davis & Linn, 2000; Ge & Land, 2004; Lai, 2008), can be an
effective way to elicit reflection since they provide cognitively complex ways learners think about, feel about, and
make connections in experience. Moon (2004) has suggested structuring reflection with questions to deepen the
quality of reflection. Amulya (2004) notes that, by engaging in reflective activities such as responding to the question
prompts, learners could build their understanding and locate the significance of their activities in a larger context.
Thus they are enabled to observe the meaning they have taken from the experience and excavate the underlying
qualities that made the experience significant. Specially, recent research shows the evidence of positive influence of
tutee’s question prompts to tutor’s reflective learning in the context of peer tutoring (Cohen, Kulik, & Kulik, 1982;
Graesser, Person, & Magliano, 1995; Roscoe & Chi, 2008). Roscoe and Chi (2008) find that tutee questions can
motivate tutor explanations and metacognition, and thus have a significant and positive influence on the tutor’s
learning activities and opportunities.
Researchers in ILEs (Intelligent Learning Environments) have also recognized the importance of incorporating
question prompts into ILE designs (e.g. Hmelo and Day, 1999). Question prompts have been used as scaffolds to
help direct students towards learning-appropriate goals, such as focusing student attention and modeling the kinds of
questions students should be learning to ask (Azevedo and Hadwin, 2005). Positive evidences are found for question
prompts to help students with various aspects, such as knowledge integration (Davis and Linn, 2000) and illstructured
problem-solving (Ge and Land, 2004; Xie and Bradshaw, 2008). Meanwhile, mechanisms for supporting
self-explanation (Aleven & Koedinger, 2002), tutorial dialog (Grassser, VanLehn et al., 2001) or reflective dialog (
Katz, O’Donnell & Kay, 2000) have been prevalent in traditional intelligent tutoring systems (ITS), in which the
computer plays the role of tutor (e.g. Cognitive Tutor, AutoTutor). However, little research has addressed question
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