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.

The rapid adoption of digital technologies has greatly increased the volume of real-world data (RWD) in education. While these data offer significant opportunities for advancing learning analytics (LA), secondary use for research is constrained by privacy concerns. Differentially private synthetic data generation is regarded as the gold-standard approach to sharing sensitive data, yet studies on the private synthesis of educational data remain very scarce and rely predominantly on large, low-dimensional open datasets. Educational RWD, however, are typically high-dimensional and small in sample size, leaving the potential of private synthesis underexplored. Moreover, because educational practice is inherently iterative, data sharing is continual rather than one-off, making a traditional one-shot synthesis approach suboptimal. To address these challenges, we propose the Cyclic Adaptive Private Synthesis (CAPS) framework and evaluate it on authentic RWD. By iteratively sharing RWD, CAPS not only fosters open science, but also offers rich opportunities of design-based research (DBR), thereby amplifying the impact of LA. Our case study using actual RWD demonstrates that CAPS outperforms a one-shot baseline while highlighting challenges that warrant further investigation. Overall, this work offers a crucial first step towards privacy-preserving sharing of educational RWD and expands the possibilities for open science and DBR in LA.

Enhancing peer interactions is a crucial topic in collaborative learning, where both the quantity and quality of interaction are emphasized. Despite its importance, the social and emotional aspects of collaborative learning, such as peer interactions, remain underexplored. This study applied learning analytics to examine how learner similarity in learning behavior, engagement, and knowledge influences the quantity and quality of peer interaction within the peer help system. Three aspects were analyzed using logistic regression and ordinal logistic regression: whether learners reply to questions, whether they evaluate the help received, and the evaluation scores given by askers to helpers. The results reveal that knowledge similarity has a significant negative impact on whether learners reply to questions, while no factors were found to significantly impact whether learners evaluate the help received. However, knowledge similarity showed a significant positive effect on evaluation scores. These findings provide insights into the complexity of learner similarity in shaping peer interactions and offer implications for designing methods that balance homogeneity and heterogeneity to enhance peer interactions in computer-supported collaborative learning environments.

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.

In many schools, teachers ensure that learners acquire academic knowledge and competences and promote their comprehensive psychological and social development through a process known as Guidance and Counseling (G&C). However, this process often involves dealing with difficult tasks and requires considerable effort from teachers. In recent years, as ICT tools have become more common, log data on daily use and learning log data have accumulated. These data have enabled teachers to understand leamer processes. Utilizing data across contexts is expected to deepen learners understanding, which is the basis of G&C. However, despite these needs and potential, the use of data for G&C has not yet been fully explored. Therefore, this study examines how the log data accumulated in the Goal-Oriented Active Learner (GOAL) system can be used by teachers to understand learners. Specifically, we extracted the characteristics of each learner's situation for various contexts from the log data, which can capture learning and daily routines. We then interviewed two teachers to determine how the visualized characteristics might be useful for G&C. The results suggest that the visualized characteristics are valuable for understanding learners' conditions both inside and outside of school, and these characteristics could support teachers' G&C activities, transcending specific subjects and activities.

This study investigates the transferability of Self-Direction behavior across different contexts and phases of learning using the GOAL system. Self-directed learning (SDL) is crucial for lifelong learning. It is significantly influenced by Self- Direction Skills (SDS), a meta-skill that is said to be transferable across different contexts, including the ability to identify learning needs, set goals, select strategies, and evaluate outcomes. Utilizing log data collected from Japanese junior high schools and analyzed using the iSAT system, we explored how Self-Direction behavior acquired in one context can be transferred to another and how these skills vary across the SDL phases. The results indicated that the Self-Direction behavior transferred between different activities and phases. In addition, the way of transfer is suggested to vary from phase and context. This study provides useful insights for the design and guidance of SDL support systems in educational programs. It suggests that it is important for educators to identify factors that facilitate the development and transfer of SDS.

This study investigates the relationship between students' scores in weekly tests and the unit exam. By analyzing score patterns, we found that students who consistently scored high on weekly tests performed better in the unit exam, whereas those who struggled with the unit's contents early tended to score lower. These findings emphasize the importance of using weekly test scores as formative assessments to help students monitor their progress and adjust their learning.

Recent studies have emphasized the importance of formative assessments in improving teaching and learning. This study investigated what insights can be gained from analyzing students' pen-stroke data in homework to support formative assessment (n=37). The results showed that pen-stroke data revealed students' thought processes, including partial understanding and trial-and-error attempts, which were not visible in the final answers. Regarding this result, one mathematics teacher interviewed expressed an interest in using pen-stroke data in the classroom, particularly in specific units. This study concluded that pen-stroke data can enhance formative assessment by offering more profound insights into student learning.

This study looks at learning habits of temporal regularity in learning activities. Building such habits involves learners' regulation of their behaviors and requires learning strategies for time management, which is a cornerstone of self-regulated learning (SRL). Given the importance of habit-building in education, Learning Analytics (LA) techniques have been applied to various long-term supports by monitoring learners' habitual behaviors from the trace data. However, building a learning habit does not always mean the productive use of time. Scant supports attend to recommending learners by building which habit can improve their learning productivity. Hence, this study proposes recommendations for productive learning habit-building from learning logs. We focus on the context of English reading in a Japanese junior high school and design an algorithm to compute a recommended learning time slot. Furthermore, we collect learners' perceptions of their productivity and learning status at different times of the day. The comparison between self-report and log data presents that learners are not aware of their learning as the detection from their learning logs. This implies the potential of the proposed recommendations for facilitating learners to build productive learning habits. Specifically, our study can suggest an optimal time in learning plans and provide learners with a sustainable cue to automate learning behaviors from long-term perspectives. By building productive learning habits, learners can become more engaged in their studies as well as lead more balanced lives.

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.

In higher education, teachers sometimes urge students to apply what they have learned during class to real-life situations. However, interweaving real-world events with class activities, or the There and Back Again process, entails difficulty in relating dynamic experiences to the course contents. This study attempted to link both sides using text mining techniques on course data from a Japanese university. We extracted key phrases from four weekly assignments featuring students' real-world explorations. Then we measured the semantic similarity between each key phrase and each course content and linked the pairs with high similarity. For the linked pairs, we conducted data analyses. We also held a semi-structured interview with a course teacher regarding the interpretability of visualized data and its practical use. Consequently, we confirmed: 1) the links between the course contents and students' key phrases appeared differently in the weekly assignments, 2) notable links between specific course contents and key phrases were identified, and 3) the visualized data contained valuable insights for the teacher, but more fine-grained linking and integrated presentations were required. Despite several limitations, the results support the potential utilization of this approach for effective data-enhanced experiential learning.

This paper introduces a system designed to enhance extracurricular learning by recommending suitable peer helpers. The system integrates educational big data of knowledge and learner models to optimize peer learning opportunities with learning analytics. Utilizing a graph-based recommendation algorithm, it incorporates three distinct indicators to dynamically match learners with suitable peers based on specific learning contexts and needs. A significant feature of the system is its capability to visualize the knowledge and proficiency levels of potential helpers, thereby empowering learners to make well-informed decisions with less bias. The system's adaptability also permits educators to tailor it to meet diverse educational goals. We are conducting an experiment with this system during a university's paper reading course to test its usability. The system aims to reduce the burden on teachers and minimize the time students spend seeking help outside of class.

Digital learning platforms equipped with behavior sensors have provided abundant educational data. Utilizing this data, learner modeling can identify assorted learner characteristics from their behavior logs for learning analytics and dynamically update them in real time. There is a growing demand for knowledge-level modeling, moving beyond behavioral logs to assess knowledge proficiency. Based on item response theories and cognitive statistical models, existing studies estimate learning rates across various knowledge elements in each learning step in intelligent tutoring systems. However, these models, tailored to specific knowledge domains, offer limited flexibility across different knowledge units and scenarios. This paper introduces an adaptation of the basic additive factor model underpinned by logistic regression, focusing on behavior indicators. Drawing upon authentic learning data from an e-book learning infrastructure for junior high math, we examine the feasibility of our adapted models and demonstrate their potential for flexibility across knowledge units and learning phases.

This study proposes indicators from daily handwritten math learning logs of junior high school students to model knowledge proficiency, and analyzes the extent of their actual correlation with proficiency using the LEAF system. Our analysis reveals that specific pen stroke behaviors, such as writing speed and task engagement time, show significant, though weak, correlations with proficiency levels. These findings suggest that handwritten logs can serve as effective indicators of student proficiency, offering valuable insights for enhancing educational outcomes.

This PDF file contains the front matter associated with ICCE Proceedings including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

This study proposes the Open Knowledge and Learner Model (OKLM), a novel framework that integrates Learning Analytics (LA) with Digital Twin (DT) technology to model learners' knowledge, internal states, and environments. The OKLM DT framework addresses the limitations of traditional LA systems by enabling accurate estimation of knowledge states and personalized learning strategies. We developed a conceptual framework for the learner DT using LA, verified the accuracy of the OKLM-based DT model, and applied it to a learning support system. Initial experiments in an English literature recommendation system showed that, while the recommendations did not significantly enhance learners' motivation, they were well-received and positively correlated with increased engagement among highly motivated learners. A subsequent study involving Intensive Reading (IR) support for EFL learners further validated the model's effectiveness. Additionally, experiments targeting educators demonstrated that the OKLM's visualization tools were valuable for understanding learner characteristics and tailoring teaching materials. These findings suggest that OKLM can enhance the versatility and accuracy of learner models across various educational contexts, offering a significant advancement in the field of LA.

Handwriting logs in the math answering process have recently been collected, and features related to the answering performance and the process, such as the stroke duration, have been investigated. However, the results reported in previous studies showed inconsistencies, and sufficient consideration had not been given to the differences in the problem types. In this study, we classified some problems into two types and verified whether there is a difference in the effect of the handwriting process on performance in each feature. The result of the analysis showed a significant difference in the effects of problems on the features used in this study, such as answering time and number of strokes. This study contributes to the need to take into consideration the problem type in learning support with handwriting process logs.

Self-directed learning (SDL) is considered a crucial skill for 21st-century learners, promoting personalized and responsive educational experiences. This study explores the untapped potential of self-reflection, particularly in e-learning environments. The research focuses on self-reflection notes, which contain strategies past students adopted when facing different situations or challenges. These notes can help current or future students facing similar situations. In this study, students take tests weekly and leave their self-reflection notes after tests. In these notes students recorded their feelings and issues, offering perspectives and insights that experts might overlook or misunderstand in some details, thus failing to provide appropriate assistance. Extracting and categorizing information from self-reflection notes is crucial to further utilize this data. Our research introduces a machine learning-based approach that effectively classifies these self-reflection notes such as cognitive, metacognitive, experiential, and irrelevant text. We compare the performance of BERT, based on the transformer architecture, with traditional machine learning classifiers such as Support Vector Machines (SVM) and Random Forests (RF). Additionally, we enhanced the BERT model by training it on synthetic data generated through GPT-4 and employing a hybrid loss combining Supervised Contrastive Learning (SCL) and Cross-Entropy (CE) to improve classification capabilities. Our results indicate that the BERT model, enhanced with advanced training techniques, outperforms traditional models in classifying learning strategies from self-reflection notes. This study not only advances the understanding of SDL in online learning environments but also demonstrates the potential of tailored machine-learning solutions to foster more effective and adaptive learning strategies.

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.

Under the societal and cultural structure where education systems are heavily driven by examinations (especially in Asia) and the pursuit of excellence reigns supreme, competing for limited educational, environmental/natural, and capital resources is prevalent and appears unavoidable. Although great socioeconomic development and scientific advances have been achieved, it inevitably comes with adverse consequences (e.g., alarmingly increasing rates of anxiety and depression, diminishing learning interest and confidence, social inequity, conflicts, divisiveness, and lack of inclusiveness). The focus of this panel is if and how 'Global Harwell' (a term combining 'harmony' and 'wellbeing') can direct all stakeholders involved in education to a universally shared educational goal. This panel invites a group of international scholars to share their practices and research findings on how the values, principles, concepts, and elements of 'Global Harwell' (e.g., positive emotions, engagement, positive relationships, meaning, accomplishment, compassion, gratitude, empathy) can be or are being actualized in educational systems, with technologies playing a supporting or an essential role at the global, societal, family, and/or individual levels, for ultimate student wellbeing and a harmonious world.

The paper focuses on the optimized use of “nudges” in an online learning platform by undergraduate students during a semester-long elective course. The study involved in a compelling exercise in which students from different academic years participated in co-designing nudges for an online learning platform to prompt behavior changes. After analyzing the student responses on designed nudges on when these nudges should be used in varying reading rates, they were categorized as confront, social, and deceive. In self-directed learning, the data analysis trends confront and social category nudges were the most preferred, while deceive category nudges were the least popular i.e. The confront category and social category nudges were found more effective than deceive category nudges for behavior change.

Educational explainable AI (XAI) applications are gaining research focus and have distinct needs in the domain of Education. This research presents Educational eXplainable AI Tool (EXAIT), a system for math quiz recommendations, along with an explanation. EXAIT was implemented in a Japanese public high school where students received the top 5 math problems based on Bayesian Knowledge Tracing (BKT) algorithm in a learning analytics dashboard. It aimed to help them complete their summer vacation assignments having 240 questions. On click, the students were redirected to an eBook platform to submit their accuracy and confidence level in each problem. We conducted a study with a quasi-experimental design and divided into 3 groups based on compliance of use. RecoExp group received and used explanations regarding why an item was recommended and how it aims to maximize learners' knowledge-gaining path. RecoCon was the control group that received just the recommendations and used it and RecoNone group did not use the system at all during the time period. We provide a framework to analyze learning logs from EXAIT and extract emerging self-directed learning patterns. Analyzing 222 students' EXAIT logs, we found learners who had checked explanations while selecting recommendations had significantly higher performance. Further differential process mining highlighted significant active daily engagement transitions of the RecoExp group in the self-directed activity.

We extracted learners' characteristic answering behaviors from handwritten process log data and investigated whether learners' situations could be inferred based on these characteristics. The result showed we were able to extract several characteristic answering behaviors, such as stopped pen stroke and late start. Furthermore, we examined the learners' situation for each feature in the actual answering process. The results revealed that several characteristic answering behaviors indicated situations such as learners' stumbling or giving up. These results imply that handwritten process log data can allow teachers to capture learners' situations and support teachers' interventions.

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.

In terms of the learning logs accumulating from real-world educational activities, techniques of learning analytics help understand the characteristics of learners' behaviors. However, extracting learning habits from log data and data-driven support for building learning habits have not yet attracted much attention. Therefore, this research proposes the approach of “Learning Habits Mining,” which aims to extract the types and stages of learning habits from learners' daily learning logs and support learners to build learning habits with data-driven methods. We identify two contributions of this research. First, this research reveals the learning habits of K12 learners and provides an approach to trace the process of building learning habits automatically. Second, this research proposes interventions to the data-driven support for building learning habits so that learners can build learning habits based on evidence derived from learning logs.

As many countries seek to promote competency-based education, formative assessments are important to capture the learning processes of learners. However, as yet there are no assessments that can fully capture the learning process. Recently, the use of ICT tools for learning has become more general, and learning log data has been accumulated. Using these data, it has become possible to capture learning processes in detail; therefore, data-driven assessment has attracted increasing attention. However, as conventional data-driven competency assessments require experts to map data to competencies, they can only be applied in a defined context. In this study, we proposed an assessment framework that allows teachers to assess their students’ competency by freely combining data collected as students used the Learning & Evidence Analytics Framework (LEAF) platform. We created an assessment in a scenario in an assumed educational setting using the proposed framework and examined what kind of assessment would be possible. Then, we created a system for the framework. Finally, interviews were conducted with three teachers regarding the system. The results suggest that the system can achieve context-independent and flexible data-driven assessment, contributing to the continuous improvement of learning and teaching from multiple perspectives in activities that use the system.

Peer evaluation of individual or group work is often adopted in team-based learning design. However, some raters may not take the evaluation process seriously and exhibit behaviors such as using the same score, rushing through evaluations, or not evaluating during the presentation. This study investigates the issue of unserious peer evaluation in group presentations, focusing on their behavior patterns. Using evaluation behavior analysis indicators, we identified unserious raters who exhibited low reliability in the peer evaluation process. Further, we conducted a preliminary analysis to detect unserious raters based on learner model data available before the peer evaluation process. This information can assist teachers in providing personalized prompts and interventions prior to the peer evaluation process, thus enhancing the evaluation quality of these students with timely prompts to them.

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.

Utilization of Information and Communication Technology (ICT) has been penetrated not only in regular classes, but also in resource rooms, or we call Special Needs Class (SNC), in Japan. Its study has been a popular research target among stakeholders for special needs education, however, there is little reported on discussing how log data can be used to support teaching and learning for children who need special support. In this paper, we propose implementation of using BookRoll (BR), a learning analytic enhanced e-book in SNC. For a pilot study, three participants who attend SNC were selected from an elementary school. Their handwritings with a touch-pen left in a memo function in BR were visible as logs in an analysis tool to illustrate the outcomes of their performance which enabled to trace their learning difficulties. Parents’ intervention as limitation and introducing Learning Evidence Analysis Framework (LEAF) system and learning support for children with special needs from the field of educational learning analytics are suggested.

Self-directed learning (SDL) is an important skill in the 21st century, while the understanding of its process in behavior has not been well explored. Analysis of the sequential behavior patterns in SDL and the relations with students’ academic performance could help to advance our understanding of SDL in theory and practice. In this study, we mined the behavioral sequences of self-directed extensive reading from students’ learning and self-directed behavioral logs using differential pattern mining technique. Furthermore, we built models to predict students’ academic performance using the conventional behavior frequency features and the behavior sequence features. Experimental results identified 14 sequential patterns of SDL behaviors in the high-performance student group. The prediction model revealed the importance of sequential patterns in SDL behavior, which was built with an acceptable AUC. These findings suggested that several SDL strategies in behavior contribute to students’ academic performance, such as analysis learning status before planning, planning before learning, monitoring after learning.

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.

Self-regulation in learning involves planning and utilizing different shared resources. This study investigates learning strategies of different student groups when they are accessing course materials in digital medium - one group is the students with high academic performance, the other is the students with low academic performance. We analyze data of 116 students from a mathematics course in a junior high school. Using the differential pattern mining technique, we highlight underlying course content accessing patterns from the learning log collected by an e-book system, BookRoll.

ENaCT is a framework for the design and analysis of critical thinking environments based on 4E cognition perspectives. In this paper, we describe a web-based critical thinking environment designed to implementœ the ENaCT framework. When users perform a critical thinking task in the environment their interaction logs are captured. We report on a pilot study with undergraduate participants and analyse how participants used the affordances in the environment as they performed the critical thinking task. One case of task-related behaviours (high activity) is elaborated to highlight the current possibilities of the system and discuss implications for redesign.

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.

Jigsaw learning is one of the popular cooperative learning methods which has been utilized in many English as Foreign Language (EFL) classrooms. In recent years, utilization of technology in education has accelerated. In this study, Jigsaw+, a new jigsaw learning approach with an e-book reader, was proposed to explore how the learning design can be applied for English vocabulary acquisition, reading comprehension, and students' engagement in group activities. A quasi-experimental study was conducted at a high school in Japan for advanced and standard class students. Participants read and comprehend a story in English through BookRoll, an e-book platform with an analysis tool embedded. Two research questions were investigated; 1) to what extent did Jigsaw+ affect students' English vocabulary knowledge and reading comprehension? and 2) to what extent did Jigsaw+ promote students' reading engagement in the jigsaw group activities? The results revealed that Jigsaw+ learning tasks assisted to improve students' vocabulary and reading comprehension for both the advanced and standard groups. Moreover, it facilitated students' engagements in activities. Some limitations of the study are also discussed.

We developed a three-level template for education and learning data usage policy in Japan and published it on the Academic eXchange for Information Environment and Strategy website. We found that clarifying the purpose of use and obtaining subjects’ consent are central to education and learning data usage, including personal information, and there is a responsibility to manage the data appropriately. The template allows for using education and learning data for research in compliance with the Act on the Protection of Personal Information. It can also be used to create anonymously processed information from such data for sharing among universities.

Summer vacation is considered a cause of loss in students' learning performance. In this study, we investigated the differences in learning behaviors in reading learning materials and time-related behavior patterns regarding summer assignments among three classes under different instructional styles. The results showed that students' learning behaviors in summer were correlated with instructional styles in school.

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.

Previous research has established that motivation has a positive impact on the learning processes and behaviors [1] [2]. Learning analytics (LA) can play an important role in addressing the issue of collecting learning behaviors. In this study, we observed the teaching activities of three classes and examined the relationships among learning motivation, learning performance, and learning behaviors of students in digital learning material readers.

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.

This paper introduces a system for collaborative learning which is designed to assist teachers in forming and grading groups for in-class group activities. The system is implemented as an extension of a learning analytics dashboard system and uses log data from a learning management system for operation. It consists of a group formation parameter console and the results console where formed groups are visualized and can be graded. The system supports teachers by using algorithms based on reliable learning evidence thereby simplifying the group formation process. All the group formation and grading data is logged thereby cyclically providing an infrastructure for subsequent collaborative learning activities.

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.

Learning log is defined as a digital record of what learners have learned in their daily lives using ubiquitous technologies. By using the ubiquitous learning system named SCROLL(System for Capturing and Remining Of Learning Logs), learners can save what they have learned in their daily lives with photo, such as location (latitude and longitude), learning place, and date and time of creation as a learning log. Although learners have many opportunities to learn words and meanings of objects with taking a photo in their daily lives, SCROLL is not implemented functions for supporting language learning with object and text detection. Therefore, this paper proposes a ubiquitous learning system to support language learning with object and text detection technologies.

In this paper, we describe the support system for job-hunting students to learn job-hunting related terms using an eBook and a chat system. Job-hunting process is very unique and complicated in Japan. Job-hunting students face difficulties in many phases. Some job-hunting related terms are not used in daily conversation and very new to them. Therefore, it is necessary to support them. In fact, many universities in Japan have started providing their students with career education. The objective of this study is to examine whether or not the use of our chat system was effective in learning job-hunting related terms. The result of the evaluation showed there was no statistically significant difference. However the highest score was given when they were asked it's helpfulness.

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.

With e-learning systems gradually being implemented, researchers worldwide have started devoting increasing attention to Learning Analytics. At Kobe university, a digital textbook reading system has been developed to collect learning logs in the face-to-face classroom. In a previous study, k-means clustering was implemented to analyze learning behavior patterns; however, there were problems such as few variables for clustering and a failure to consider weighting of the learning elements. Therefore, in this study we applied clustering by increasing the number of learning elements and assigned weights to the learning elements, then analyzed the learning behavioral patterns. We found some behavioral patterns of students who can save learning time if they effectively write memos and add markers.

Online learning environments presently accumulate large amounts of log data. Analysis of learning behaviors from these log data is expected to benefit instructors and learners. This study was intended to identify effective measures from e-book materials used at Kyushu University and to employ these measures for analyzing learning behavioral patterns. In an evaluation, students were grouped into four clusters using k-means clustering, and their learning behavioral patterns were analyzed. We examined whether the learning behavioral patterns exhibited relations with the learning outcomes. The results reveal that the learning behavior of “backtrack” style reading exerts a significant positive influence on learning effectiveness, which can aid students to learn more efficiently.

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.

We propose a new change detection method using clickstream data collected through an e-Book system. Most of the prior work has focused on the batch processing of clickstream data. In contrast, the proposed method is designed for online processing, with the model parameters for change detection updated sequentially based on observations of new click events. More specifically, our method generates a model for an individual student and performs minute-by-minute change detection based on click events during a classroom lecture. We collected clickstream data from four face-to-face lectures, and conducted experiments to demonstrate how the proposed method discovered change points and how such change points correlated with the students' performances.

In this paper, we present a system framework making use of e-book logs for visualization learning support systems intended to provide meaningful learning environment for e-book learners. An ontology-based visualization support system, which supports not only meaningful reception learning but also meaningful discovery learning, is designed and developed to help e-book learners to effectively construct their knowledge frameworks. Three main functions are provided in this personalized visualization support system: (1) for any knowledge of E-books, the learner can get its e-book location information and a relation map including its relevance knowledge and their relations; (2) for any page range of any e-book, the learner can get a relation map including knowledge shown in those pages, their relevant KPs and their upper concepts; (3) for any learning period, the learner can check the knowledge map including the knowledge they had read, the relevance knowledge and their relations. Compared to the passive reception environment, in meaningful discovery learning environment learners are encouraged to actively locate new knowledge in their own knowledge framework and restructure existing knowledge by detecting hidden relations between relevant KPs through reflecting the attributes of acquired knowledge visually. Meanwhile the iterative procedure of confirmation and modification in their own relation map ensures that they check the logical consistency of their ideas and clear up misunderstandings.

A real-time learning analytics system is proposed for in-classroom use. We used an e-learning system and an e-book system to collect real-time learning activities during lectures. The collected logs were analyzed and presented visually on a web-based system for the teacher. The teacher can monitor how many students are viewing the same page as the teacher, whether they are following the explanation, or if they are reading previous or subsequent pages. Through a case study, we confirmed the effectiveness of the real-time learning analytics system, in terms of high synchronization between the teacher and the students, i.e., that the majority of students followed the teacher's explanation and added more bookmarks, highlights, or notes on the e-book, compared with the control group where the teacher did not use our system.

To discover teaching knowledge efficiently, we must extract the various teaching activities from educational data. In this paper, through the use of e-book logs and techniques of time-series analysis, we describe a method of practicing teaching analytics in face-to-face classes, one which enable us to extract the teaching activity efficiently and accurately.

Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course "Information Science" held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts.

The Learning Analytics and Policy (LAP) workshop explores and documents the ways in which policies at national and regional level are shaping the development of learning analytics. It brings together representatives from around the world who report on the circumstances in their own country. The workshop is preceded by an information gathering phase, and followed by the authoring of a report. The aspirations, achievements and constraints in the different countries are contrasted and documented, providing a valuable resource for the future development of learning analytics.

Many universities choose the C programming language (C) as the first one they teach their students, early on in their program. However, students often consider programming courses difficult, and these courses often have among the highest dropout rates of computer science courses offered. It is therefore critical to provide more effective instruction to help students understand the syntax of C and prevent them losing interest in programming. In addition, homework and paper-based exams are still the main assessment methods in the majority of classrooms. It is difficult for teachers to grasp students' learning situation due to the large amount of evaluation work. To facilitate teaching and learning of C, in this article we propose a system---LAPLE (Learning Analytics in Programming Language Education)---that provides a learning dashboard to capture the behavior of students in the classroom and identify the different difficulties faced by different students looking at different knowledge. With LAPLE, teachers may better grasp students' learning situation in real time and better improve educational materials using analysis results. For their part, novice undergraduate programmers may use LAPLE to locate syntax errors in C and get recommendations from educational materials on how to fix them.

Student's learning happens where the learner is, rather than being constrained to a single physical or digital environment. It is of high relevance for the LAK community to provide analytics support in blended learning scenarios where students can interact at diverse learning spaces and with a variety of educational tools. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers in other areas, interested in the intersection between ubiquitous, mobile and/or classroom learning analytics. The underlying concern is how to integrate and coordinate learning analytics seeking to understand the particular pedagogical needs and context constraints to provide learning analytics support across digital and physical spaces. The goals of the workshop are to consolidate the Cross-LAK sub-community and provide a forum for idea generation that can build up further collaborations. The workshop will also serve to disseminate current work in the area by both producing proceedings of research papers and working towards a journal special issue.

This paper describes seamless learning analytics methods of VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs). VASCORLL is a system for visualizing and analyzing the learning logs collected by the seamless learning system, which supports language learning in the real-world. As far, several studies have been made in the seamless learning environments in order to bridge formal learning over informal learning. However, their focus was the implementation of the seamless learning environment in education. This study focuses on visualizing and analyzing learning logs collected in the seamless learning environment. This paper describes how our analytics could contribute to bridging the gap between formal and informal learning. An experiment was conducted to evaluate 1) whether our developed VASCORLL is effective in connecting the words learned in formal learning to the ones learned in informal learning, 2) which social network algorithm is effective to enhance learning in the seamless learning environment. Twenty international students participated in the evaluation experiment, and they were able to increase their learning opportunities by using VASCORLL. In addition, it was found that the betweenness centrality is useful in finding central words bridging formal and informal learning.1

In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.

In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.

This paper describes visualization and analysis methods using educational big data collected by research project at Kyushu University in Japan. The project uses an e-book system called BookLooper, Moodle, and Mahara. Logs for this analytics were collected from 99 first-year students in an information science course at Kyushu University. The number of logs are collected approximately 330,000, and this paper visualize and analyze the collected logs. The purpose of this study is to predict students' final grade and to profile visualization and analysis results. The prediction of this study shows that it leads to discoveries of students who fail to make the grade.

This paper describes our study where we aim to support onomatopoeia learning for Japanese language learners using the ubiquitous learning log system called SCROLL (System for Capturing and Reminding Of Learning Log). We have improved our system by adding a new function EPUB (Electronic PUBlication) in order to improve quiz efficiency and to enrich its contents. Our pilot evaluation indicates that the quiz function worked sufficiently. However, some user-unfriendliness was pointed out.

Seamless learning is an approach in which learners can build a relationship between their formal learning and informal learning. One of the issues of seamless learning is the way to connect formal learning with informal learning. To tackle this issue, we propose an e-Book based system in this paper. Our proposal learning system has two functions. One is an e-Book viewing function which can log what learners learned from e-Books as learning logs. The other is a seamless learning function which enables users to connect formal learning with informal learning. In this paper, we introduce our system and its evaluation.

This paper describes an innovative visualization method called Seamless Learning Visualization (SLV). As far, several researchers have introduced in the seamless learning environments in order to bridge formal learning over informal learning. However, their focus was the implementation of the seamless learning environment. Our proposed SLV is to visualize and analyze learning logs collected in the seamless learning environment. This paper describes how our visualization method could contribute to bridging the gap between formal and informal learning. An experiment was conducted to evaluate (1) whether SLV would be beneficial in terms of usability in finding words in the seamless learning environment and (2) Which visualization layout (Random layout, Force-directed layout, Yifan-multilevel layout and SLV) is effective in supporting learning in the seamless learning environment. Fifteen international students participated in the evaluation experiment in order to evaluate (1) and (2). In the experiment, it was found that SLV was very beneficial in terms of usability and effectiveness for learning than previous visualization methods.

This research aims to investigate the relationship between self-regulated learning awareness, learning behaviors, and learning performance in ubiquitous learning environments. In order to do so, psychometric data about self-regulated learning and log data such as marker, annotation, accessing device types that stored the learning management system were collected and analyzed using multiple regression analysis with stepwise method. The results indicated that self-efficacy, internal value, and the number of read slides had a significant influence on the final score, and the awareness of cognitive learning strategy use has slightly significant power to predict the final score.

It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.

This paper proposes an approach of the spatio-temporal data mining in order to predict next learning steps (next ubiquitous learning logs to be learned) in accordance with their situations or context from past learners' experiences in their daily lives accumulated in the ubiquitous learning system called SCROLL (System for Capturing and Reminding of Learning Log). Ubiquitous learning log (ULL) is defined as a digital record of what learners have learned in their daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and reuse ULL with others. This paper describes some data mining methods using the association analysis in order to detect effective and efficient learning logs for learner from relationships among ubiquitous learning logs collected by a number of the research studies for a long period of the SCROLL project (2011~2014).

This paper discusses learning behavior analysis using a learning management system (LMS) and an e-textbook system. We collected a large number of operation logs from e-textbooks to analyze the process of learning. In addition, we conducted a quiz to check the level of understanding. In our study, we especially focus on an analysis of the relationship between learning behavior in informal learning and its effectiveness in the corresponding quiz. We apply a machine learning and classification methodology for behavior analysis. Our experimental results demonstrate that students who undertake good informal learning achieve better scores in quizzes.

This paper describes our study in progress where we have aimed to enhance career support for international students using the learning log system called SCROLL (System for Capturing and Reminding Of Learning Log). Job-hunting process is very unique in Japan. Students should begin the job hunting in their junior year and it takes 8.9 months on average. A questionnaire survey reveals that top two anxieties that international students have are language-related and lack of information. In order to reduce these anxieties, we propose career support system using SCROLL. The result of its pilot evaluation demonstrates that the users benefited from the system and that it will be expected to contribute to the solution of the two anxieties, while it reveals some user unfriendliness in its interface. Our future works include its improvement as well as the refinement of its message and recommendation functions.

This preliminary research investigates the relationship between psychometric data and learning behaviors in the learning analytics research field, specifically, the relationship between self-regulated learning and learning behavior. The results of this limited research show that marker and annotation use have a weak significant relationship with self-efficacy and the intrinsic value of learning materials.

Analyses on students’ learning behaviors comprise an important thrust in education research. This study focused on e-books system used in the classroom and this system recorded students’ learning logs in their daily academic life. These learning logs can be used to analysis students’ learning behaviors. By performing partial correlation analysis, the study found that a number of learning behaviors have a significant relation with students’ test scores.

This paper describes a method of the visualization and analysis for mining useful learning logs from numerous learning experiences that learners have accumulated in the real world as the ubiquitous learning logs. Ubiquitous Learning Log (ULL) is defined as a digital record of what learners have learned in the daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and reuse ULL with others. By constructing real-world corpora which comprise of accumulated ULLs with information such as what, when, where, and how learners have learned in the real world and by analyzing them, we can support learners to learn more effectively. The proposed system will predict their future learning opportunities including their learning patterns and trends by analyzing their past ULLs. The prediction is made possible both by network analysis based on ULL information such as learners, knowledge, place and time and by learners' self-analysis using time-map. By predicting what they tend to learn next in their learning paths, it provides them with more learning opportunities. Accumulated data are so big and the relationships among the data are so complicated that it is difficult to grasp how closely the ULLs are related each other. Therefore, this paper proposes a system to help learners to grasp relationships among learners, knowledge, place and time, using network graphs and network analysis.

This paper proposes a learning log dashboard interface design issues for a mobile learning system called SCROLL (System for Capturing and Reusing Ubiquitous Learning Log). SCROLL allows learners to record and share their daily learning experiences as ULL (ubiquitous learning logs) with locations, photos, words, sentences and/or videos using their smartphones. Moreover SCROLL provides quiz that are generated from ULL so that they can remember past learning experiences. Learners add many words, photos on their account and always repeat this action, like as continuous training. It will be sometimes happened forgetting quiz even works on those words in the past. The overall goal of this learning log dashboard is to enable learners to reflect on their own activity, to reinforce what they have learned and to learn from each other. Learners also can see the information about her/his progress on learning log dashboard.

This paper describes a system that can be used to visualize some ubiquitous learning logs to grasp and discover several learning flow and timing. Visualization of the system is based on vast amount of learning data in ubiquitous learning environment. Ubiquitous Learning Log (ULL) is defined as a digital record of what learners have learned in the daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and to reuse ULL with others. This paper will reveal about the relationship between the ubiquitous learning logs and learners by using network graph. Also, this paper will explicate the system through which learners can grasp their learning time, histories, knowledge and location.

In this paper we have explored flipped classroom. How we can encourage students to learn outside-class is a key issue in flipped classroom. In our previous study it was found that our system called SCROLL (System for Capturing and Reminding Of Learning Log) contributed to the students’ increased involvement in outside-class learning. SCROLL is expected to play an important role in its effective implementation of flipped classroom. A pilot evaluation was conducted to examine the effectiveness of our proposed learning scenario using SCROLL and SNS(Social Networking System). The result showed that it enhanced the students’ outside-class learning, though more examination is necessary to conclude its contribution since there are other factors which contributed to boosting up outside learning time. Timing of posting outside-class task was also examined and the period of two weeks was found to be appropriate.

Nowadays, it is a hot topic to analyze the huge amount of data in the world. This issue also exists in the learning during students’ life. The learning data are collected only to record students’ learning status. As a result, most learning data are not used to improve the quality of learning for students. In this paper, we propose an order made education system, which can recommend students to select the courses they want to learn. In order to analyze students’ learning behaviors, we collect students’ learning data by using mobile devices.

In this paper, we discuss the addition of technologically supported informal social networks for the exchange of previously implicit information attached to documentation within formal organisational frameworks. Focusing on the implementation of technologies to support this social interaction, we first look at why social networks are a topic discussed in recent times, then we put forward plans for a new system developed for this purpose.

In this paper, we present plans for a retooled ubiquitous computer system that works towards facilitating knowledge acquisition and knowledge dissemination between learners during the process of acculturation. Focused on the foreign population of JAIST (Japan Advanced Institu te of Science and Technology - a Japanese post graduate university in Japan), the system provides a platform on which to study the behaviour of participants, and also the process of acculturation dynamically in context. In addition, the study works toward s understanding the feasibility of using such ubiquitous systems as possible support mechanisms in the future. In the current global environment, human beings via their own experiences acculturate at different speeds, and with different levels of success. By incorporating ubiquitous technology into the environment in which people are acculturating, we provide a new way to analyse the process of acculturation dynamically, and provide assertions as to how the system may benefit users in the future.

This paper introduces a mechanism in a ubiquitous learning log system, which is designed to guide learners to participate in the learning activities recommended by the system. In order to provoke learners’ interests on the knowledge, the recommended knowledge is related to both what learners are studying and the learners’ current learning contexts. And to help learners grasp the knowledge, in the second step the mechanism provides learners with relevant learning activities to guide learners to use the recommended knowledge.

In our previous works, we developed a system named SCROLL in order to log, organize, recall and evaluate the learning log. However up to now, we just use an active mode to record logs. This means that a learner must take a capture of learned contents consciously and most of learning chances be lost unconsciously. In order to solve this problem, we started a project named PACALL (Passive Capture for Learning Log) in order to have a passive capture using SenseCam. With the help of SenseCam, learner’s activity can be captured as a series of images. We also developed a system to help a learner find the important images by analyzing sensor data and images processing technology. Finally, the selected images will be uploaded to the current SCROLL system as ubiquitous learning logs. This research suggests that SenseCam can be used to do passive capture of learning experiences and workload of reflection can be reduced by analyzing sensor data of SenseCam.

This study primarily exploits a context-awareness and personalization model supporting ubiquitous learning log system. Learning log stands for the log of knowledge or learning experience acquired ubiquitously. The model has three main behaviors, which are to recommend learning objects in accordance with both learners’ needs and contexts, to detect their learning styles using the context history and to prompt them to review what they have learned regarding their learning styles. What’s more, by monitoring learners’ reaction on the recommendation or prompting, the model can improve its prediction.

This paper proposes a ubiquitous learning log system called SCROLL (System for Capturing and Reminding Of Learning Log). Ubiquitous Learning Log (ULL) is defined as a digital record of what you have learned in the daily life using ubiquitous technologies. It allows you to log your learning experiences with photos, audios, videos, location, QR-code, RFID tag, and sensor data, and to share and to reuse ULL with others. Using SCROLL, you can receive personalized quizzes and answers for your questions. Also, you can navigate and be aware of your past ULLs supported by augmented reality view. The initial evaluation of applying this system in an undergraduate English course is illustrated.

This paper proposes a Social Networking Service site based mobile environment for learning foreign languages called SONLEM, which supports learners to find a partner who can solve the language learning problems at the online community, and an appropriate request chain of friends will be recommended upon their request. The learner can practice his second language with a native speaker who is learning his language.

Searching for knowledge is an essential problem that learners face while learning. In a ubiquitous learning environment, it is difficult for a learner to find the right knowledge in time even if the source of this knowledge is very near to his physical location. Therefore, this paper presents a personalized collaborative ubiquitous learning environment in order to support the learner while doing a task. It allows the learners to share knowledge, interact, collaborate, and exchange individual experiences. This environment is called PERKAM. It utilizes the ubiquitous technologies to recommend the educational materials and the peer helpers according to the learnerpsilas current task and location. This paper focuses on the user model and the recommendation methodology.

The existence of wireless and mobile technology has provided opportunities to support learning regardless of the physical learning space. Thus for example, usage of mobile terminals such as smart PDA (personal digital assistant) in a learning system is very common phenomenon. Mobile devices can communicate with wireless mobile terminal and realize the intelligence exchange with its surroundings. Based on this current trend and the need to realize learning in a more dynamic environment, a LOCH (Language Learning Outside the Classroom) system named One Day Trip is developed to assist overseas students to learn Japanese in real life situations with the help of a PDA. This paper proposes the task assignment support function in the system to aid the teacher to give learners appropriate tasks in the learning environment.

The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the right thing at the right time in the right way[1]. A ubiquitous and mobile computing environment enables people to learn at any time and any place. Nevertheless, the fundamental issue is how to provide learners with the right information at the right time in the right way. This paper tackles the issues of right time and right place learning (RTRPL) in a ubiquitous computing environment.

The continuous development of wireless and mobile technologies has allowed the creation of an additional platform for supporting learning, one that can be embedded in the same physical space in which the learning is taking place. This paper describes a computer supported ubiquitous learning environment for language learning, called LOCH (Languagelearning Outside the Classroom with Handhelds). In the environment, the teacher assigns field activities to the students, who go around the town to fulfill them and share their individual experiences. The main aim of this project, called One Day Trip with PDA, was to integrate the knowledge acquired in the classroom and the real needs of the students in their daily life.

During learning computer science theory, it is essential to learn sorting algorithms, but it is not easy to understand the concept of the different sorting algorithms. This paper describes a system called PSSLSA (PDA-based Participatory Simulation System for Learning Sorting Algorithms). This is an interactive simulation system to learn the sorting algorithms. Learners use it to deeply understand the sorting algorithms. Using this system, the teacher can assign tasks to his student and ask them to sort a list of numbers according to a certain algorithm. Learners receive these tasks, collaborate together and send the result to the server. The system will check it and feedback the student with the positions of the numbers if there is a mistake. The learners will correct the number positions and send it back to the server. Learners can understand the algorithm through the dissections and their errors.

The distribution of all kinds of resources in e-learning environment requires more conscious learning services for learner's personal or collaborative learning. Satisfying personalized needs on knowledge contents and recommending the most potential knowledge collaborators for effective collaboration are regarded as the two most significant learning services. This paper proposes to use Knowledge Awareness (KA) to support learning services providing focused on Personalization and Collaboration in e-learning space. This paper presents a three-dimensional KA model, illustrates a KA-supported learning services providing mechanism, and also provides a simple case study.

Online real/virtual classrooms/schools will play a more important role in the society of the future. Especially, an anytime/anywhere learning environment, which allows learners to participate in a classroom via networks, is one of the most focused theme of this research field. In such a trend of these networked learning environments, the authors have already proposed a new concept for the asynchronous virtual classroom that combines online learning materials with both asynchronous communication tools and software agents. The software agents, which are simulated classmates in a classroom situation, act for real learners by means of utilizing past activities of past learners. In addition to such agents' behavior as semi-real classmates, they could be configured as purely virtual classmates by a lecturer, too. Now that they reconsidered and reconfigured a flexible architecture for software agents, this paper reports its basic design and an application for agent-based classmates in asynchronous learning scenario.

We focus on an XML-based video annotation system for language learning using a videoconference system. The environment supports the native speaker as teacher and non-native learner. We extend the videoconference system so the system can record the learning scene as a video file. The learning scene recorded in the videoconference system can be used for instruction. For instance, the teacher can explain by inserting feedback as annotation in the scene. If a teacher edits the video for explanation, it is very useful as a teaching material. However, an ordinal video editing system creates quite a new video file so it cannot change the contents for playback. In addition, the learner can hardly grasp the teacher's task. Therefore, we propose a video correction and VCML (Video-based Correction Markup Language) based on XML to represent corrected information. We call the prototype Viclle (Video-based Communicative Language Learning System). With the system, both teacher and learner can communicate via e-mail without sending video files. In addition, video correction resembles retrieving information from a database with keywords. Therefore, Viclle is regarded as an error database.