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Computer Science, Machine Learning

Fine-Tuning Deep Learning Models for Automated Answer Grading in Japanese

Fine-Tuning Deep Learning Models for Automated Answer Grading in Japanese

In this study, the researchers aim to address the urgent task of improving the articulation between high school and university in Japan by analyzing the current system and proposing reforms. The authors argue that the current system is inadequate and hinders students’ smooth transition from high school to university, leading to a lack of coordination and continuity in their education.
To address this issue, the researchers propose several reforms, including:

  1. Changing the entrance examination system to a more holistic approach that considers students’ overall abilities and potential, rather than just their academic performance.
  2. Introducing a credit transfer system that allows students to earn credits from high school and apply them to their university studies, ensuring a more seamless transition.
  3. Improving communication between high schools and universities through regular meetings and exchange programs for teachers and students.
  4. Developing a more comprehensive counseling system that provides students with personalized advice on their academic and career choices.
    The authors also discuss the challenges of implementing these reforms, such as addressing resistance from vested interests and overcoming bureaucratic obstacles. They emphasize the need for a coordinated effort from various stakeholders, including government, educators, and industry, to create a more coherent and effective articulation system.
    By providing a detailed summary of the article, this response aims to demystify complex concepts by using everyday language and engaging metaphors or analogies to capture the essence of the research without oversimplifying.