Vocabulary learning that incorporates visual information has become widely recognized as an alternative to context-based methods. However, few studies focus on learners' incorrect answers. On the Other hand, fossilization caused by repeated errors has been a concern. Our proposed system, L-VEIGe, effectively prevents repeated errors by visualizing learners' incorrect answers through image generation, which encourages introspection. However, there exists a 'Feature Disappearance' problem, where the generated images for incorrect answers lack sufficient information for comprehension. This study proposes a method for quantitatively evaluating these error images from a cognitive perspective.

Research surveys have always posed a challenge for novice researchers who lack research training. These researchers struggle to understand the directions within their research topic and the discovery of new research findings within a short time. One way to provide intuitive assistance to novice researchers is by offering relevant knowledge graphs $(KG)$ and recommending related academic papers. However, existing navigation knowledge graphs mainly rely on keywords or meta information in the research field to guide researchers, which makes it difficult to clearly present the hierarchical relationships, such as inheritance and relevance between multiple related papers. Moreover, most recommendation systems for academic papers simply rely on high text similarity, confusing researchers as to why a particular article is recommended. They may lack the grasp of important information about the insight connection between ‘Issue resolved’ and ‘Issue finding’ that they hope to obtain. This study aims to support research insight surveys for novice researchers by establishing a hierarchical tree-structured knowledge graph that reflects the inheritance insight and the relevance insight among multiple academic papers on specific research topics to address these issues.

With the exponential growth in the number of papers and the trend of AI research, the use of Generative AI for information retrieval and question-answering has become popular for conducting research surveys. However, novice researchers unfamiliar with a particular field may not significantly improve their interaction efficiency with Generative AI because they have not developed divergent thinking in that field. This study aims to develop an in-depth Survey Forest Diagram that guides novice researchers in divergent thinking about the research topic by indicating the citation clues among multiple papers to help expand the survey perspective for novice researchers.

Novice researchers often face difficulties in understanding a multitude of academic papers and grasping the fundamentals of a new research field. To solve such problems, the knowledge graph supporting research survey is gradually being developed. Existing keyword-based knowledge graphs make it difficult for researchers to deeply understand abstract concepts. Meanwhile, novice researchers may find it difficult to use ChatGPT effectively for research surveys due to their limited understanding of the research field. Without the ability to ask proficient questions that align with key concepts, obtaining desired and accurate answers from this large language model (LLM) could be inefficient. This study aims to help novice researchers by providing a fish-bone diagram that includes causal relationships, offering an overview of the research topic. The diagram is constructed using the issue ontology from academic papers, and it offers a broad, highly generalized perspective of the research field, based on relevance and logical factors. Furthermore, we evaluate the strengths and improvable points of the fish-bone diagram derived from this study's development pattern, emphasizing its potential as a viable tool for supporting research survey.

Characteristics of Highly Sensitive Persons (HSPs), such as “depth of processing,” “overstimulation,” “emotional reactivity and empathy,” and “sensitivity to subtleties,” often present challenges due to their high Sensory Processing Sensitivity (SPS) to environmental stimuli. This study investigates the differences in SPS among learners and the impact of various avatars on video presentations, a medium that has seen increased use due to COVID-19. We surveyed 20 participants who engaged with SDG instructional videos featuring four different avatars. Using the HSPS-J19 self-assessment tool, analysis of their SPS responses revealed a normal distribution of SPS scores, indicating individual differences. Additionally, correlations were found between HSPS-J19 scores and participants' impressions and motivation regarding avatar presentations. Cluster analysis results suggested that the group with a higher tendency towards HSP traits benefited more from appropriate avatar use. Based on these findings, we designed an online lecture support environment that allows for the control of video stimuli. This research explores an underexamined area and aims to enhance online lectures for HSPs, who constitute approximately 15% to 20% of the population. Supporting high SPS learners is particularly significant in the post-COVID-19 era.

STGs, a longstanding video game subgenre, have grown more intricate over time, deterring new players. To address this, a training system is required to improve character control skills in STG games. Bayesian Knowledge Tracing (BKT) is a common approach researchers use to monitor and assess students' progress. While BKT is effective in evaluating intellectual knowledge, it falls short in assessing character control skills in STG, a form of motion knowledge. This study proposes a Skill Tracing (ST) model that combines BKT approaches to monitor both cognitive knowledge and character control abilities. Results indicate its superiority in skill-tracking tasks over traditional BKT, offering a more accurate prediction of players' skill levels.

With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically extracting specific patterns in complex and unstructured layout documents. Current data mining methods for academic articles employ rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods incurs a high coding cost for complex typesetting articles. On the other hand, simply using machine learning methods requires annotation work for complex content types within the paper, which can be costly. Furthermore, only using machine learning can lead to cases where patterns easily recognized by rule-based methods are mistakenly extracted. To overcome these issues, from the perspective of analyzing the standard layout and typesetting used in the specified publication, we emphasize implementing specific methods for specific characteristics in academic articles. We have developed a novel Text Block Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid. We used the well-known ACL proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% classification accuracy and 90% detection accuracy for tables and figures.

The final objective of this research project is to develop a lecture archive summarization platform that can extend learners' experience by automatically estimating and providing temporal and spatial ROI (Regions of Interest) according to multimodal features and learners' learning logs in lecture archives that record face-to-face lectures. To develop this platform, we (a) establish a method for extracting spatiotemporal multimodal features of lecture archives and (b) construct a method for estimating a learning style model based on the learning logs when watching the archives with the extracted features. Furthermore, to maximize the learning effect, we will (c) develop a prototype system to adaptively control the spatiotemporal ROI at the terminal side according to the learning style model as an adaptive summarization. This article describes the concept of the proposed platform and the initial analysis of learners' learning logs.

Due to the COVID-19 pandemic, most universities endeavored to adopt online education as an alternative to conventional face-to-face classroom instruction. However, capturing students' Temporal Region of Interest (T-ROI) in long-duration video lectures poses a significant challenge. Therefore, lecture archive summarization becomes essential from an online perspective. The results of lecture archive summarization still require further improvement. This research aims to distinguish T-ROI using a speech processing approach h. Our plan is divided into collecting instructors'/presenters' voice datasets, clarifying the T-ROIs through sound processing technology, and building a suitable deep neural network architecture to detect the T-ROIs in the actual lecture archives automatically. We will inevitably encounter various challenges to achieve the objective, such as individual differences. This article describes the experimental dataset collection design considering individual differences and lecture room environments. It summarizes how such efforts will be effective in realizing personalized voice style detection and improving the accuracy of speech processing in real environments.

Virtual reality technology has the potential to revolutionize immersive experiences in various applications, including office settings. However, efficient text entry in VR remains a significant challenge. This study addresses this challenge by proposing a machine learning-based solution, the 2S-LSTM typing method, to enhance text entry performance in VR. The 2S-LSTM leverages the back of the hand image. It employs a two-stream Long Short-Term Memory (LSTM) network, combined with a Kalman Filter (KF), to improve hand position tracking accuracy and reduce jitter. The results from questionnaire-based evaluations and typing data analysis demonstrate the superiority of the 2S-LSTM solution over existing solutions like Oculus Quest 2 and Leap Motion in terms of typing efficiency, fatigue reduction, accurate hand position replication, and positive user experience. These findings contribute to the advancement of text entry in VR environments and pave the way for immersive work experiences in the office and beyond.

In Web-based investigative learning, learners are expected to construct wider and deeper knowledge by navigating a great number of Web resources/pages. In elaborately investigating an initial question, learners are expected to decompose an initial question into related question to be further investigated. However, it is difficult for learners to conduct question decomposition in concurrence with their knowledge construction. In our previous study, we have proposed a model of Web-based investigative learning, and developed the system named interactive Learning Scenario Builder (iLSB for short). Although iLSB could promote self-directed investigative learning, learners often decompose a question into unrelated sub-questions. This suggests the necessity of promoting reflection on question decomposition by diagnosing the appropriateness of question decomposition. Toward this issue, we have proposed a method for diagnosing the appropriateness of question decomposition with Linked Open Data (LOD). In this paper, we describe an adaptive prompting with diagnosed results for reflection on question decomposition. This paper also reports a case study whose results suggest the potential for promoting reflection on improper question decomposition.

There currently exist a lot of Web resources, which are useful for learning. However, it is hard for learners to learn the Web resources since the hyperspace is not always well-structured. Our approach to this issue is to mine collective knowledge from a group of learners who learned the Web resources to reconstruct the hyperspace including useful pages and links to be learned. This paper proposes a collective knowledge mining method that can extract these pages and links from learning histories gathered from the group of learners.