One way or another, students face certain difficulties and disadvantages of distance learning. This can affect students physically and emotionally if appropriate measures are not taken. The solution might be the personalization of distance learning process, which implies the application of individual approaches to learning. The article aims to determine the academic performance of students who use the system of personalized distance learning Quiz Guide. For this study, 140 students were selected. The results of the performance analysis showed that the experimental group scored higher on the final test, indicating that students using the Quiz Guide learning system had significantly better academic achievement than those who took the traditional approach to learning. According to the post-test results, the control group scored (average) 65.14 points, the experimental group, 74.71.
Keywords: Academic performance, computer learning system, individualized approach, personalized distance learning, Quiz Guide system.

/pdf/personalized-distance-learning-experience-in-determining-3cvlv7ry.pdf

Personalized distance-learning experience in determining students’ performance

Human guides in museums and galleries are professionally trained to stimulate informal 
learning in visitors by asking low-risk, open-ended reflective questions
that enable them to
focus on specific features of artifacts, 
relate to prior experiences, and elicit curiosity as well as further thought. We present ArtMuse, our AI-powered chatbot for asking reflective questions in context of paintings. Our reflective question generation model in ArtMuse
was trained by applying a
novel combination of existing models for
extractive question answering and open-domain chitchat. User evaluation studies indicate that we are able to generate fluent and specific reflective questions for paintings that are highly-engaging.

/pdf/generating-reflective-questions-for-engaging-gallery-28qygg63.pdf

Generating Reflective Questions for Engaging Gallery Visitors in ArtMuse

/pdf/quiz-generation-on-the-electronic-guide-application-for-4ke2xrpfr5.pdf

Quiz Generation on the Electronic Guide Application for Improving Learning Experience in the Museum.

In this paper, we propose a BERT model for understanding cultural properties in museum. BERT is a language model used in natural language processing. Generally, BERT is pre-trained on a large corpus and then fine-tuned on a specific task. Recently, additional learning for BERT has been reported to improve task performance in fine tuning. Therefore, we propose a BERT model for understanding cultural properties in museum (MinpakuBERT) by additional learning a BERT model using document data from the National Museum of Ethnology. To evaluate the performance of MinpakuBERT, we compared the performance of MinpakuBERT and pre-trained BERT by fine-tuning them on two tasks related to cultural properties. The two tasks for comparison were classification tasks using the OCM (Outline of Cultural Materials) label for function and the OWC (Outline of World Cultures) label for region. As a result, the performance of MinpakuBERT is higher than the pre-trained BERT model in the two tasks. In addition, the learning speed in fine tuning was also faster for MinpakuBERT than for the pre-trained BERT. The MinpakuBERT was published on a model-sharing website and made available as a language resource for anyone to use.

MinpakuBERT: A Language Model for Understanding Cultural Properties in Museum

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Quiz Generation on the Electronic Guide Application for Improving Learning Experience in the Museum.

MinpakuBERT: A Language Model for Understanding Cultural Properties in Museum