Hello everyone. This is Hirata, a first-year master’s student at Yamada Laboratory. Happy New Year. I look forward to working with you all again this year.
This is my first post of the year for our English paper seminar. The paper I read is as follows:

Title: nStudy: Tracing and Supporting Self-Regulated Learning in the Internet
Journal: International Handbook of Metacognition and Learning Technologies, 293–308
Springer International Handbooks of Education
Authors: Philip H. Winne and Allyson F. Hadwin
Year: 2013

In recent years, using the internet for gathering information, writing reports, and studying has become commonplace.

It is said that high “Self-Regulated Learning” skills are required to learn using online information sources. Self-Regulated Learning refers to learners actively regulating their own learning activities. To grasp how much one understands about the learning content and to think about what needs to be learned, “Self-Regulated Learning” skills are necessary, and they are an essential element when learning using the vast amount of information on the internet. In this paper, a web learning application called nStudy was developed to improve learners’ Self-Regulated Learning skills.

nStudy is a system where web browsers like Chrome are extended for learning, and learners study using HTML content. As extensions, a note-taking tool to record what has been learned, a keyword tagging function for text, and a collaborative learning function with other learners were adopted. Furthermore, the system’s operation logs are recorded in millisecond units, and by analyzing that data at the behavioral level and presenting it to the learners, Self-Regulated Learning was supported. Finally, as a future challenge for the developed nStudy system, the addition of a feedback function to improve learning skills and activate self-regulation of learning was mentioned.

From here on are my thoughts after reading this paper. This paper assumes Self-Regulated Learning in individual learning situations, and in the system design, it emphasizes that learners autonomously improve their learning based on data. Since the system I am developing also assumes a design where learners optimize their own learning, I would like to actively refer to this point.

Specifically, I would like to refer to the mechanism of collecting operation logs in millisecond units and providing feedback or evaluating learning methods to learners based on their transition patterns. My research aims to promote the use of learning strategies that emphasize foreign language audio, so I thought I would like to utilize transition graphs using such detailed operation logs to evaluate the degree of use of learning strategies and provide feedback to learners.

On the other hand, there are points I did not understand. This paper does not mention effective learning strategies for learning with the system, and it is unclear based on what criteria (data) recommendations regarding learning methods are presented to learners. I felt it is necessary to clarify effective learning methods when systematically inputting information from the internet and then consider recommendations that encourage them.