Tokyo Gakugei University (JAPAN)
About this paper:
Appears in: INTED2022 Proceedings
Publication year: 2022
Pages: 8837-8844
ISBN: 978-84-09-37758-9
ISSN: 2340-1079
doi: 10.21125/inted.2022.2311
Conference name: 16th International Technology, Education and Development Conference
Dates: 7-8 March, 2022
Location: Online Conference
Students need to learn proactively to develop competencies. During exercise solving, a student is required to master knowledge and skills while learning proactively by himself/herself. To do this, a student is required to learn in a self-regulated fashion. For students to learn proactively during exercise solving without self-regulation, they require something by their side to facilitate proactive learning. The purpose of this study is to facilitate proactive learning during exercise solving.

To facilitate proactive learning, a student needs to constantly reflect on his/her learning in order to adjust tasks and progress to subsequent learning. Encouraging a student to ask himself/herself questions effectively enables the student to reflect on his/her own learning. Therefore, we focused on prompting as a way to encourage a student to ask himself/herself questions.

We also investigated the effectiveness of providing prompts via an interactive robot on reflection on learning and clarified the effectiveness of promoting the cognitive awareness of a student by just providing prompts regardless of context of learning. Therefore, we aim to facilitate proactive learning by an interactive robot during exercise solving.

In this study, we developed an application that runs inside an interactive robot. We used an interactive robot named RoBoHoN.

The application had four functions.
Function 1 provides a student with prompts depending on the student’s responses and learning states. In this study, we set three learning states named, “Solved,” “Unsolved,” and “Can be solved”. By using this function, a student should be able to be promoted to ask himself/herself questions.
Function 2 records learning states selected by a student when he/she finishes solving an exercise. By using this function, the interactive robot should be able to support a student depending on learning.
Function 3 recommends an exercise or a part for a student to select depending on his/her own learning states. By using this function, a student will be able to consider prospects for what kind of exercises or parts he/she should try.
Function 4 visualizes the number of “Can be solved” exercises and displays it as a progress graph on a LCD panel on the back of RoBoHoN. By using this function, a student will be able to realize that he/she is definitely progressing and move on to subsequent learning by increasing the number of “Can be solved” exercises.

We conducted a preliminary experiment using our developed application to clarify its effectiveness. Participants were four students at a university in Japan.

We interviewed students after the preliminary experiment. The results of this interview revealed the following advantages of our developed application.
- A student asked himself/herself questions and progressed to subsequent learning.
- A student attempted to solve exercises while interacting with the interactive robot as a partner.
- A student was trying to solve exercises diligently thanks to RoBoHoN providing prompts.
- A student attempted to solve exercises while considering prospects thanks to RoBoHoN providing prompts by his/her side.
The results indicated that students could learn proactively by using our developed application during exercise solving.

For future work, we will perform a more detailed evaluation utilizing additional practices for high school students on the basis of the preliminary experiment and further analyze the effectiveness of our developed application.
Interactive robot, reflection on learning, self-regulation, exercise solving, artificial intelligence in education.