Educational assessment requires understanding student problem-solving processes, not just final answers. Current AI-driven analytics focus on static outcomes, missing valuable insights from temporal dynamics. Explain-from-Stroke is a practical framework that captures invisible learning processes by integrating handwriting dynamics with vision-language models. The system extracts temporal features such as writing speed, pauses, and revisions, providing additional context for generating meaningful insights into hidden aspects of student reasoning. Using real classroom data from a Japanese secondary school, the model shows an 18.2% improvement in cognitive depth analysis compared with static approaches. This work provides educators with an accessible method to analyze learning processes using standard tablet technology.

Background and context : Open datasets play a crucial role in three prominent research domains that intersect data science and education: learning analytics, educational data mining, and AI in education. Researchers in these domains apply computational methods to analyze data from educational contexts, aiming to better understand and improve teaching and learning. Research scope and gap : Providing open datasets alongside research papers supports research reproducibility, fosters collaboration, and increases trust in research findings. It also provides individual benefits for authors, such as greater visibility, credibility, and citation potential. However, despite these advantages, the availability of open datasets and the associated practices within the learning analytics research communities, especially at their flagship conference venues, remain unclear. Goal and method : To address this gap, we conducted a systematic survey of publicly available datasets published alongside research papers in learning analytics domains. We manually examined 1,125 papers from three respected flagship conferences (LAK, EDM, and AIED) over the past five years (2020–2024). We discovered, categorized, and analyzed 172 unique datasets used in 204 publications. Results and contributions : Our study presents the most comprehensive collection and analysis of open educational datasets to date, along with the most detailed categorization. Of the 172 datasets identified, 143 were not captured in any prior survey of open data in learning analytics. We provide insights into the datasets’ context, analytical methods, use, and other properties. Based on this survey, we summarize the current gaps in the field. Furthermore, we list practical recommendations, advice, and 8-item guidelines under the acronym PRACTICE with a checklist to help researchers publish their data. Lastly, we share our original dataset: an annotated inventory detailing the discovered datasets and the corresponding publications. We hope these findings will support further adoption of open data practices in learning analytics communities and beyond.

In online mathematics education, self-explanation is increasingly recognized as a key tool for improving learning outcomes. Identifying learning impasses, which present significant educational challenges, is crucial. Typically, detecting these impasses demands considerable effort from educators to manually review and identify issues in students’ mathematical reasoning.This paper introduces a fully automated impasse detection system designed for online math learning that leverages self-explanations. The system collects high-quality data from students working on the same quizzes, generates example answers, and uses these as benchmarks to identify where students are struggling. The system architecture is described in detail, focusing on the methods used to gather and validate high-quality self-explanation data.Empirical analysis using text regression models shows promising results: the models predict self-explanation scores with an error rate of 0.585 for validation data and 0.655 for evaluation data. While there are variations in scoring accuracy across different mathematical topics, the findings suggest that the system has the potential to significantly improve mathematics education by automating the detection of learning impasses.

In the field of mathematics education, self-explanation is recognized as a critical facilitator for learners to articulate their understanding of complex mathematical concepts and problem-solving techniques. With the emergence of digital learning platforms, the potential to utilize such self-explanations for automated evaluation has expanded, yet significant challenges remain. This study introduces a method that integrates vision and language models to enhance the accuracy of automated evaluations of self-explanations in mathematics quizzes. By leveraging the CLIP encoder, we utilize features from both handwritten images and textual self-explanations, aiming to incorporate the characteristics of handwritten solutions that have been overlooked by text-only evaluations. Models were developed to include self-explanations alone (baseline) and those that integrate image features, using both the original and a fine-tuned CLIP encoder adapted to our dataset of self-explanations and handwritten images. Experimental results demonstrated that the model utilizing the fine-tuned CLIP significantly outperformed the baseline, showing a notable reduction in MAE. Conversely, the model employing the original CLIP encoder exhibited decreased performance compared to the baseline, revealing the complex interplay between integrating self-explanations and image features. These findings suggest that the benefits of embedding image features depend on the quality and appropriateness of the visual data incorporated.

Post-exam reflection is critical in helping students consolidate knowledge acquired during a course, enabling them to apply this understanding in future professional contexts. This study investigates the effectiveness of Mirai, a large language model-based (LLM) chatbot, in supporting students' post-exam reflection needs in an Automation Engineering course. Through a controlled experiment, we explored how context-tuned and non-context-tuned versions of Mirai impacted students' reflection habits, help-seeking behaviors, and perceptions of the tool. Students interact with the chatbot to clarify exam questions and receive personalized explanations. A a surveys based on the extended technology acceptance model (exTAM) was conducted and the resulting data was analyzed. We assessed the efficacy of Mirai in facilitating a deeper understanding of exam-related material, improving students' knowledge, engagement and performance. The findings from this study provide insights into the immediate educational benefits of LLM-based tools, their acceptance among students, and their role in enhancing learning outcomes in engineering education.

This study will explore and experiment with various combinations of methods to handle data imbalance in order to address the common issue of insufficient minority samples in at-risk student prediction. Additionally, we will examine the purpose of applying computer tools to educational issues and emphasize the necessity of adhering to models with high transparency and explainability, ensuring that the decision-making process can be transparent and comprehensive in the context of learning analytics. After comparing model performance, we selected the logistic regression model combined with correlation analysis and threshold adjustment, which showed outstanding performance in UAR, G-means, and other evaluation metrics. We will analyze the reasons behind students' academic performance based on the feature importance ranking from the model, thereby establishing a high-performance and high- transparency benchmark model for the LBLS593 dataset.

Extensive Reading (ER) is recognized for enhancing English proficiency in EFL learners. Although understanding speed is essential for analyzing reading behavior, limited research has focused specifically on reading speed during ER sessions. This study addresses this issue by analyzing log data from junior high school students engaged in ER activities. Using agglomerative hierarchical clustering, we identified four distinct reading profiles based on Word Per Minute (WPM) changes. Future studies should further explore the impact of these profiles on the effectiveness of ER.

The Educational Research Data Platform (EREDA) developed at Kyoto University addresses the challenges of managing and analyzing educational data while prioritizing privacy and security. EREDA seamlessly integrates with the existing Learning and Evidence Analytics Framework (LEAF) and offers advanced data management and analysis capabilities, including a robust Data Integrity Checker (DIC) tool and backfilling. By employing real-time data streaming, robust anonymization techniques, and a multi-tenancy design, EREDA empowers researchers to conduct secure and efficient educational research. The platform also features collaborative tools that enhance knowledge sharing and accelerate insight generation. As EREDA continues to evolve, its potential to drive data-driven decision-making in educational institutions and influence educational policies is significant. Future developments will focus on scaling the platform to reach a broader user base and enhancing its functionality to better support researchers.

The rapid development of Generative AI (GenAl) provides more opportunities and methods to deliver meaningful, engaging and gamified learning experiences to language learners. While there are various language learning applications, current methods often suffer from low completion rates and a painful learning process. In this paper, we propose a new gamified learning experience for English Language learners based on an image-text-image GenAl game: AVERY (Augmenting Vision to Enhance YouR English writing skills). The game is designed to enhance learner engagement by adopting image generation in English writing. A learner begins by providing the system with an image. The learner can ask the AI for hints to describe the image and pass a well-curated sentence to the system. The system generates an image based on the learner's answer. In the final round, the system provides feedback on how well the learner provided useful and correct clues and areas for further improvement. 12 respondents were asked to play the game and fill a questionnaire. The results showed a positive affect towards the AVERY system and its use in enhancing learner engagement.

Learning EFL through translation tasks is an effective language learning technique, but requires consistent practice and scaffolding. This study evaluates TAMMY, a prototype EFL chatbot designed for Japanese learners to practise English translation tasks. Using the extended Technology Acceptance Model, the study examines Tammy's usability, usefulness, and enjoyment. Response appropriateness and task success are also explored. Findings from a pilot study with Japanese university students indicate high usability and positive attitudes towards the chatbot. Tammy effectively provided accurate feedback in most tasks successfully guiding learners to an accurate translation, though improvements are needed in feedback clarity and conversational adaptability. Despite limitations, Tammy shows promise as a support tool for language learning, offering an engaging and non-judgmental platform for practising translation and enhancing English proficiency.

This study aims at modeling and visualizing students' behavior in a self- regulated and unguided learning environment with a focus on learning progression. Since the modeling approach is process-oriented and does not depend on specific mastery learning criteria, this paper provides a novel way to identify wheel-spinning in self-regulated learning solely based on activity monitoring. The study investigates the free and unsupervised engagement of junior high school students in solving mathematics exercises during summer vacation. During this period, a pool of exercises was provided on the LEAF online learning platform. Additionally, the students receive adaptive exercise recommendations as an add-on. Guided by the basic idea of wheel- spinning as persistent engagement without learning progression, we have designed a mathematical model and a graphical representation to capture and gauge the individual learning progression. Based on an expert questionnaire survey, we considered how this novel representation can serve as a basis to analytically characterize learning progression and specifically to identify wheel-spinning.

Providing explanations in educational recommender systems are supposed to increase students’ awareness of the recommendations, trust toward the system, motivation to adopt the recommendations. With the expectation to have a higher prediction accuracy, more and more complex recommendation models are developed, which are difficult to explain. It remains debatable that whether there exists a trade-off between the accuracy and explainability of recommender systems. In this study, we focus on the explainable math quiz recommender system--- Naïve Concept Explicit (Naïve CE) proposed in our previous work. We are interested in knowing whether the explainable Naïve CE has a good prediction accuracy compared with a powerful but less explainable model--- Matrix Factorization (MF). We also proposed a combined model CE+MF to preserve the explainability of Naïve CE and predicting power of MF. We then used a long-term quiz answering dataset to evaluate the models’ accuracy as to predicting students’ correctness rate of the quizzes. The results revealed that 1) The explainable model Naïve CE had a lower accuracy than the less model MF given the sparse dataset; 2) Combining two models achieved a moderate accuracy in predicting students’ answers while preserving the explainability of Naïve CE. Our study served as an example of how to develop an inherently explainable educational recommender system and how to improve the accuracy by integrating more complex models.

Many systems to assist in learning English grammar have been developed in the field of learning material recommendation systems (LMRSs). Compared with those based on knowledge models, recommendations based on data tend to cause the cold-start problem, and it is said that explainable LMRSs may be able to enhance learners’ motivation. In our research, we propose an explainable English grammar quiz recommendation system using a knowledge map to support students’ learning of English grammar with trust in the system and motivation. The learning effect of the explanation of the system was evaluated in an experiment, in which 349 high school students in Japan participated. This experiment showed that there was little learning effect of the explanation, but the system reliability and motivation improved by the explanation. The limitation and our future work regarding the validity and learning effects of the system are also indicated.

This paper reports an innovative data capturing pipeline for learner modeling by integrating learning logs with physiological and environmental sensor data. The learning logs are collected from BookRoll, an ebook reader. Wearable device signals consist of the user’s affective state from a new version of the eye movement tracking device JINS Meme. An Omron sensor was used to collect environmental data like temperature, noise level, humidity, and luminescence. The architecture of the data collection and its potential are presented in this paper: As a pilot study participants did calculation and comprehension tasks in the ebook reader in two environmental conditions: one within a research lab and one at a cafe to simulate self-study environments. The data from the sensors were collected and synchronized to provide descriptive statistics.

Explainable recommendations, which provide explanations about why an item is recommended, help to improve the transparency, persuasiveness, and trustworthiness. However, few research in educational technology utilize explainable recommendations. We developed an explanation generator using the parameters from Bayesian knowledge tracing models. We used this educational explainable recommendation system to investigate the effects of explanation on the summer vacation assignment for high school students. Comparing the click counts of recommended quizzes with and without explanations, we found that the number of clicks was significantly higher for quizzes with explanations. Furthermore, system usage pattern mining revealed that students can be divided to three clusters— none, steady and late users. In the cluster of steady users, recommended quizzes with explanations were continuously used. These results suggest the effectiveness of an explainable recommendation system in the field of education.

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.

In this paper, we propose an interactive drawing based activity in BookRoll, an e-book-based teaching-learning platform. Participants who were researchers drew an overview of their research as a handwritten memo. We demonstrate the affordances of the learning analytics (LA) enhanced platform to facilitate practice and research of such a drawing activity focused on a higher-order thinking task to organize knowledge (research overview in this case). We share the multi-modal data of 4 participants to illustrate the outcomes of the activity and their perception of the activity. An initial analysis of the handwritten memo is visualised. Further reflections show how the use of learning analytics-based platforms can enrich the learning design of a knowledge organisation task.

Learning vocabulary is a crucial but challenging activity for English as a foreign language learners, and computer-assisted language learning can facilitate this process. Moreover, e-learning is attracting a great deal of attention as a new technology to bring educational support which traditional learning systems cannot provide. Recommender systems as its implementation have been subject to discussion. In this study, we propose a comprehensive learning analytics-based platform for efficient vocabulary learning, including an e-book reader and a book/quiz recommender. The system on this platform estimates learners' knowledge based on their activities and brings personalized recommendation and its bases to the learners. Also, this platform provides teachers with visualized feedback regarding the recommendation and students' engagement in learning.

Digitized learning materials are a core part of modern education and also can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open ebook test from the perspective of performance and identifying strategies that are used by both high and low performing learners. We create models to predict the performance of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that 1) strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment; and 2) low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment.

Self-direction skills in the context of learning can be supported with data in this digital era. This study analyzes the behaviors of learners during a self-directed reading and summarization task. Our work investigates an undergraduate course (n=72) where students worked on a reading and summarizing assignment while planning and monitoring the task in GOAL, a platform synthesizing learner's activity data from learning and physical activity contexts. This study focuses on the initial cohort analysis of the students' behavior based on the fine grain interaction data collected in the different systems using visual analytics techniques. Such a data-rich narrative of self-directed in-semester activities is not discussed yet in the literature to our knowledge. We discuss the implications of the trends that is found in our collected dataset for designing AI-support for self-direction skills with the GOAL platform and the scope of deeper analysis to further understand the process.

In this paper, we aim to explore students' study approaches (e.g., deep, strategic, surface) from the logs collected by an electronic textbook (eBook) system. Data was collected from 89 students related to their reading activities both in and out of the class in a Freshman English course. Students are given a task to study reading materials through the eBook system, highlight the text that is related to the main or supporting ideas, and answer the questions prepared for measuring their level of comprehension. Students in and out of class reading times and their usage of the marker feature were used as a proxy to understand their study approaches. We used theory-driven and data-driven approaches together to model the study approaches of students. Our results showed that three groups of students who have different study approaches could be identified. Relationships between students' reading behaviors and their academic performance is also investigated by using association rule mining analysis. Obtained results are discussed in terms of monitoring, feedback, predicting learning outcomes, and identifying problems with the content design.

Reading, be it intensive or extensive, is one of the key skills required to master English as a foreign language (EFL) learner. Computerized e-book systems provide convenient access to learning materials inside and outside class. Students may regularly check the meaning of a word or expression using a separate tool to progress on their reading, which is not only disruptive but can lead to other learning problems. An example of a particular issue faced in EFL is when a student learns an inappropriate meaning of a polysemous word for the context in which it is presented. This is also a problem for teachers as they often need to investigate the cause. In this paper, we propose a smart dictionary integrated into an e-book reading platform. It allows the learner to search and note word definitions directly with the purpose of reducing context switching and improve vocabulary retention. Finally, we propose that learner interactions with the system can be analyzed to support EFL teachers in identifying possible problems that arise through dictionary use while reading.

For the 21st century learner, developing self-direction skill is crucial for both academic activities and maintaining one's healthy lifestyle. While there are technology supports for specific self-regulated learning tasks and health monitoring, research is limited on how to support development of meta-skill of self-direction process itself. In our work, we focus on designing seamless technology infrastructure to foster self-directedness of learners. We consider learning and physical activities data as a context and DAPER (data collection-analyze-plan-execution monitoring-reflect), as a data-driven self-direction skill execution and acquisition model. We bridge Learning Analytics and Quantified-Self approaches to develop the GOAL (Goal Oriented Active Learner) system to support synchronize-visualize-analyze multisource data regarding learners' learning and physical activities. This paper proposes a measurement rubric as a basis of adaptive scaffolding for skill development during the process.

Students' learning behaviors in an online learning environment can be automatically recorded by learning systems. Such learning records provide new opportunities to model students' learning process. On the other hand, it has become more common to see students having wearable devices that assist in tracking their personal physical activities. These activity tracking can be integrated into a data-rich context for training students for developing their data-informed self-direction skills. We are building the GOAL (Goal Oriented Active Learner) system to support the development of self-direction skills using learning and health activity data. A key phase in any self-directed activity is goal setting and planning. This paper will introduce how to build a new model for self-planning and support the acquisition of planning skills in the GOAL system. We combine learners’ data from the self-directed activity and their interaction trace to build the model in the GOAL system. The modeling involves computing of trend value and degree of plan difficulty, then diagnosis of planning skills using a 5-point scoring criteria. An adaptive support is selected based on the computed score. The contribution of this work is modeling planning and promoting planning skills in a data-driven manner. Our approach grounds the theory of self-direction skills and enables learners to develop the skills in everyday life.

In this paper, we analyzed the relationships between students’ engagement and academic performance in the digital textbook system. To measure students’ engagement, we first extracted features from the students’ digital textbook reading logs (click-streams) that represent their engagement with the contents. Then, we used percentile rank transformation to create normalized engagement scores and an overall engagement score. In the analysis, we first investigated the correlation between engagement scores’ and the students’ final scores. Second, we modeled students’ transition patterns from the engagement to academic performance by using Markov Chains. Third, we analyzed engagement patterns of the students with different academic performance levels. Our results showed that there is a positive moderate correlation between students’ academic performance and their engagement with digital textbooks. Our results also revealed that a single engagement score can be used to measure students’ engagement with the system, which is easy to understand by non-expert users. We also introduced our dashboard interventions that are developed based on this engagement score.

In this paper, a model for identifying e-book reading style is proposed and applied onto a learning log dataset. Learning log data available as non-structured data source is processed to identify patterns of reading exhibited by users using three main structures: reading sessions, reads and passages. These structures are used to extract information on users’ reading style to be used as part of user modeling process. The proposed model is applied on a set of log data generated by university students during one semester of digital resource use. The findings show students adopt predominantly receptive reading style, while responsive style occurs rarely. Further analysis revealed no significant relationships between reading style variables and student academic success for the Architecture course indicating the variables of responsive and receptive reading bring new information as part of user modeling.

E-books are becoming a popular medium for delivering learning materials in the globe. The gap between e-books and pedagogical practices has been highlighted since teachers do not generally integrate the e-book tool into their instruction in a way that facilitates student learning. In particular, new pedagogies in language teaching and learning tend to encourage students to acquire knowledge and use the language in real-life situations. Knowledge sharing with collaborative tasks in the class can be useful. Studies on knowledge sharing, specifically in language education, are limited. To this end, this study proposes a knowledge sharing-based e-book system to enhance students’ learning performance in an EFL course. This study adopts a quasi-experimental design. Seventy-one freshmen were recruited from two classes of a freshman English course at a university in northern Taiwan. The implication from this study finding might help teachers to identify suitable technology based on the learning needs of students, and help consider the ability of teachers to adopt appropriate technology and to fit specific learning activities.

Learning English as a foreign language is a core part of K-12 education for many countries in which English is not the main spoken language, and especially in Asia. One of the fundamental tasks that students encounter is to learn vocabulary that is a part of the assigned curriculum. These are often sourced from reference materials or assigned vocabulary lists and may not consider the learner’s current proficiency or the semantic context of words that were recently learnt. By suggesting vocabulary that have similar proficiency or semantic contexts to what a student has recently studied could improve and support vocabulary learning. In this paper, we propose a method for recommending words that have similar difficulty and semantic context with previous words learnt based on the analysis of prescribed textbooks for Japanese junior high school students. This research could be used to guide a student learning English by helping them select a sequence of vocabulary that is appropriate.

Learning Analytics (LA) platforms can gather data from the teaching-learning interactions during a course. While there have been previous discussions regarding the individual tools, limited scholarship describes the utility of a LA framework for supporting evidence-based teaching-learning practices. We have proposed LEAF, a framework to bridge that gap. We implement the framework in a platform by integrating LMS, learning behaviour sensors such as an ebook reader, learning analytics dashboard and an evidence portal through Learning Tools Interoperability (LTI). The platform was then made available to teachers from different colleges in India to orchestrate their course offering for one semester. This paper describes the design of the teacher training module for the adoption of the platform based on the A2I2 model as its theoretical basis. The A2I2 model explicitly focuses on encouraging scholarship of learning and teaching among participating teachers and thus is an ideal candidate for utilizing an evidence-based framework.

In this paper, we propose an E-Book Page Ranking (EBPR) method to rank e-book pages from the original learning material automatically. The proposed method ranks all the e-book pages by the class probabilities retrieved from machine learning models. The top-ranked e-book pages are then selected to form the pre-class reading (preview) recommendation. The proposed method extracts image features and text features from e-book page contents as well as the e-book usage features from students’ previous reading logs. In this paper, we test the performance of the proposed model with two different cases, with and without past e-book usage data. The experimental results showed the improvability of the model after taking into account learners’ past e-book usages.

Current technology enables tracking of various learning and physical activities. User can use the data to analyze issues in the execution of those activities. Current work focuses on this analysis phase of data-informed self-directed activity cycle and proposes a measurement framework of the skill while learners work in a data-rich context. It is a paradigm shift to support and measure analysis skill from previous approaches which mostly rely on questionnaire-based measurements. In our approach, we emphasize the monitoring of learner’s analytical process and the automatic evaluation of the analysis results through system. Based on that, an automated measurement is carried out in the system to depict learner’s analysis skill and changes of skill. Additionally, we elaborate the framework in the context of the GOAL system which provides affordances of analysis based on physical and reading activity data.

As Learners move from one learning environment to another, there is a key necessity of taking with them a proof of previous learning achievements or experiences. In most cases, this is either expressed in terms of receipt of scores or a certificate of completion. While this may be sufficient for enrollment and other administrative decisions, it poses some limitations to the depth of learning analytics and consequently a slow onboarding process. Also, with different institutions having their learning data isolated from each other, it becomes more difficult to easily access a learner's learning history for all learning activities on other systems. In this paper, we propose a blockchain based approach for connecting learning data across different Learning Management Systems (LMS), Learning Record Stores (LRS), institutions and organizations. Leveraging on unique properties of blockchain technology, we also propose solutions to ensuring learning data consistency, availability, immutability, security, privacy and access control.

The secondary use of medical data to improve medical care is gaining much attention. We have analyzed electronic clinical pathways for improving the medical process. The analysis of clinical pathways so far has used statistics analysis models, however as issue remains that the order, and multistory spatial and time relations of the each factor could not be analyzed. We constructed an Outcome tree system that shows the greatest significant relation for each factor. The Hip replacement arthroplasty clinical pathway was analyzed by the system, and the outcome variance of the clinical pathway was visualized. The results indicate the path of patient's who have a long hospitalization stay and extracted four critical indicators.

Although previous research has demonstrated the benefits of the “learning by searching” strategy, there is a new problem which is how to measure and analyze the effectiveness of "Learning by Searching" behaviors. In this paper, by using the record of the students’ learning history, we have proposed a SNSearch system to analyze student web-searching behaviors of "Learning by Searching".

When extracting information from a web page, IE systems usually need to perform pattern recognition to identify the elements that have similar patterns. However, most of them are mainly based on analyzing HMTL source code, DOM tree, tag tree or Xpath of web pages. These methods are language-dependent, or more precisely, HTML-dependent. They have some insuperable limitations. In order to overcome these limitations, we propose a notion of layout-tree and a pattern recognition method to identify visual blocks with similar visual pattern using layout tree. In this paper, we call a visible rectangular region in a web page a visual block or block for short. We consider if the elements of two blocks are displayed in a similar layout, we define that the two blocks are visually similar. We first transform the layout into a layout tree. By calculating the similarity of the layout trees of two blocks, we can determine whether the two blocks are visually similar or not. The result of experiment shows that the layout tree is an effective method to identify visually similar blocks.

Kyushu University installed a private cloud system, named “campus cloud system”, using VCL and CloudStack. For a graduate school exercise course on web search engine, the authors prepared a virtual machine on VCL, which had apache web server and GETA indexer preinstalled. This paper introduces an outline of the cloud system, the exercise, and also reports advantages and disadvantages of cloud based education.