Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Publishing type：Research paper (international conference proceedings)   Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

Learning analytics, blending education theory, psychology, statistics, and computer science, utilizes data about learners and their environments to enhance education. Artificial Intelligence advances this field by personalizing learning and providing predictive insights. However, the opaque ’black box’ nature of AI decision-making poses challenges to trust and understanding within educational settings. This paper presents a novel visual analytics method to predict whether a student is at risk of failing a course. The proposed method is based on a dendritic neuron model (DNM), which not only performs excellently in prediction, but also provides an intuitive visual presentation of the importance of learning behaviors. It is worth emphasizing that the proposed DNM has a better performance than recurrent neural network (RNN), long short term memory network (LSTM), gated recurrent unit (GRU), bidirectional long short term memory network (BiLSTM) and bidirectional gated recurrent unit (BiGRU). The powerful prediction performance can assist instructors in identifying students at risk of failing and performing early interventions. The importance analysis of learning behaviors can guide students in the development of learning plans.

DOI： 10.1007/978-981-97-5495-3_14

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Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

In actual university education, students’ performance prediction is important for assessing their mastery of specific knowledge areas and providing feedback. To address the limitations of existing grade or at-risk predictions that fail to take students’ knowledge mastery into account, we propose a model, question&answer knowledge attention for exam performance prediction (QAKAP) and focus on predicting students’ performance on each final exam question (binary prediction). This model consists of three key modules: a Topic-Aware Attention Module, a Knowledge Mastery Estimation Module, and a Prediction Module. By integrating natural language processing (NLP) methods and the attention mechanism, our model can capture the relevance of questions from question text data and then generate the student embeddings to predict student performance on each final exam question. The primary contribution of this work is the development of a general prediction model for real university education, applicable to students’ exam performance predictions. We collected a dataset from 494 students across four undergraduate courses, including the question text data and students’ performance on these exam questions. The results show that QAKAP outperforms other machine-learning methods, demonstrating its effectiveness in predicting students’ exam performance.

DOI： 10.1007/978-3-031-72315-5_26

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Publishing type：Research paper (international conference proceedings)   Publisher：Communications in Computer and Information Science  

This study investigates the effects of generative AI on the knowledge comprehension of university students, focusing on the use of ChatGPT strategies. Data from 81 junior students who used the ChatGPT worksheet were collected and analyzed. Path analysis revealed complex interactions between ChatGPT strategy use, e-book reading behaviors, and students’ prior perceived understanding of concepts. Students’ prior perceived understanding and reading behaviors indirectly affected their final scores, mediated by the ChatGPT strategy use. The mediation effects indicated that reading behaviors significantly influenced final scores through ChatGPT strategies, indicating the importance of the interaction with learning materials. Further regression analysis identified the specific ChatGPT strategy related to verifying and comparing information sources as significantly influenced by reading behaviors and directly affecting students’ final scores. The findings provide implications for practical strategic guidance for integrating ChatGPT in education.

DOI： 10.1007/978-3-031-64315-6_12

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Publisher：CEUR Workshop Proceedings  

With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades.

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Publishing type：Research paper (international conference proceedings)  

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Other Link： https://dblp.uni-trier.de/rec/conf/lak/2024w

Publishing type：Research paper (international conference proceedings)  

In recent years, due to the impact of Coronavirus disease (COVID-19), digital platforms have developed rapidly and accumulated a large amount of data. To better utilize the comprehensive and diverse data stored in online platforms for data mining, such as learning behavior analysis or performance prediction, and to provide guidance and valuable feedback for educator became more important. For the current analysis of learning behaviors by time series data with DNN method, the interpretability is not enough. This paper proposes a method based on the simultaneous use of learning behaviors and learning materials to obtain the representation of learned knowledge, and through multiple cross-validations, the effect of this knowledge representation has a certain improvement on the original data, and the interpretability can promote the feedback function.

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Publisher：CEUR Workshop Proceedings  

In recent years, due to the impact of Coronavirus disease (COVID-19), digital platforms have developed rapidly and accumulated a large amount of data. To better utilize the comprehensive and diverse data stored in online platforms for data mining, such as learning behavior analysis or performance prediction, and to provide guidance and valuable feedback for educator became more important. For the current analysis of learning behaviors by time series data with DNN method, the interpretability is not enough. This paper proposes a method based on the simultaneous use of learning behaviors and learning materials to obtain the representation of learned knowledge, and through multiple cross-validations, the effect of this knowledge representation has a certain improvement on the original data, and the interpretability can promote the feedback function.

Scopus

Publishing type：Research paper (scientific journal)   Publisher：IEEE Access  

Student performance prediction was deployed to predict learning performance to identify at-risk students and provide interventions for them. However, prediction models should also consider external factors along with learning activities, such as course duration. Thus, we aim to distinguish the difference factor between the time dimension (duration of the course) and the feature dimension (students' learning activities) by attention weights to provide helpful information and improve predictions of student performance. In this study, we introduce Attention-Based Artificial Neural Network (Attn-ANN), a novel model in educational data mining. The Attn-ANN combines attention weighting on the time and feature dimensions to examine the significance of lectures and learning activities and makes predictions by visualizing attention weight. We found that the Attn-ANN had a better area under the curve scores than conventional algorithms, and the attention mechanism allowed models to focus on input selectively. Incorporating the attention weighting of both the time and feature dimensions improved the prediction performance in an ablation study. Finally, we investigated and analyzed the model's decision, finding that the Attn-ANN may be able to create synergy in real-world scenarios between the Attn-ANN's predictions and instructors' expertise, which underscores a novel contribution to engineering applications for interventions for at-risk students.

DOI： 10.1109/ACCESS.2024.3429554

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Language：Others   Publishing type：Research paper (other academic)  

Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

Publisher：31st International Conference on Computers in Education, ICCE 2023 - Proceedings  

Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

Scopus

Publishing type：Research paper (international conference proceedings)  

Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

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Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English  

DOI： 10.1111/1759-7714.15014

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Journal of Educational Computing Research  

This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with “an RNN model with a long-term time-series in which the features during the entire course are inputted” and the student model in KD with “an RNN model with a short-term time-series in which only the features during the early stages are inputted.” As a result, the RNN model in the early stage was trained to output the same results as the more accurate RNN model in the later stages. The experiment compared RNN-FitNets with a normal RNN model on a dataset of 296 university students in total. The results showed that RNN-FitNets can improve early prediction. Moreover, the SHAP value was employed to explain the contribution of the input features to the prediction results by RNN-FitNets. It was shown that RNN-FitNets can consider the future effects of the input features from the early stages of the course.

DOI： 10.1177/07356331221129765

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：IEEE Transactions on Learning Technologies  

In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are estimated to be insufficiently understood by each learner and the webpages related to those pages are recommended. As a method for estimating such pages, we consider extracting the pages related to the questions that were answered incorrectly. We examined the accuracy of matching each question with the pages of the learning materials. We also conducted an experiment to verify the usefulness of the system and its effect on learning using a review dashboard. In the experiment, the evaluation of the review dashboard indicated that at least half of the participants found it useful for most types of feedback. In addition, the rate of change in quiz scores was significantly higher in the group using the review dashboard, which indicates that using the review dashboard has the effect of improving learning.

DOI： 10.1109/TLT.2022.3225206

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Publishing type：Research paper (international conference proceedings)  

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Other Link： https://dblp.uni-trier.de/rec/conf/edm/2023

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Publishing type：Research paper (international conference proceedings)   Publisher：IFIP Advances in Information and Communication Technology  

Teaching and learning from one another is one of the most effective ways for learners to acquire proactive learning attitudes. In this study, we propose a new learning support system that encourages mutual teaching and learning by introducing a mechanism that guarantees sustainability. Learners submit articles called “learning-articles” that summarize their own learning and knowledge. The proposed system not only accumulates and publishes these articles but also has a mechanism to encourage the submission of necessary topics. The proposed system has been in operation since the academic year 2020, and it has collected learning-articles across our university’s nine academic disciplines from more than 300 learners. To investigate the effects of sharing learning-articles on education from the learners’ perspectives, a questionnaire was distributed among 25 students.

DOI： 10.1007/978-3-031-43393-1_44

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Publishing type：Research paper (international conference proceedings)   Publisher：IFIP Advances in Information and Communication Technology  

Smart agriculture has assumed increasing importance due to the growing age of farmers and a shortage of farm leaders. In response, it is crucial to provide more opportunities to learn about smart agriculture at agricultural colleges and high schools, where new farmers are trained. In agricultural education, a system is used for managing environmental information, such as temperature and humidity, obtained from sensors installed in the field. However, it is difficult to make effective use of this system due to the time required to detect changes in the field interfering with class time and the problem of oversight. In this study, we proposed a field environment digest system that will help learners by providing the summarized field sensing information, and support them in analyzing the data. In addition, to examine the potential for using field sensing information in agricultural education, we investigated the usefulness of the summarized sensor information and students’ usage of this information. In this paper, we outline the contents of the developed system and the results of the digest evaluation experiments.

DOI： 10.1007/978-3-031-43393-1_10

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Publishing type：Research paper (international conference proceedings)  

In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

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Publisher：30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings  

In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

Scopus

Publishing type：Research paper (international conference proceedings)  

Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

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Publisher：30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings  

The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

Scopus

Publishing type：Research paper (international conference proceedings)  

The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

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Publisher：30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings  

Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

Scopus

Language：Others   Publishing type：Research paper (other academic)  

The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

Language：Others   Publishing type：Research paper (other academic)  

In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

Language：Others   Publishing type：Research paper (other academic)  

Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Sustainability (Switzerland)  

The COVID-19 pandemic has led to an increase in online classes, and programming classes are no exception. In such a learning environment, understanding every student’s programming process is mostly impractical for teachers, despite its significance in supporting students. Giving teachers feedback on programming processes is a typical approach to the problem. However, few studies have focused on visual representations of the evolution process of source-code contents; it remains unclear what visual representation would be effective to this end and how teachers value such feedback. We propose two feedback tools for teachers. These tools visualize the temporal evolution of source-code contents at different granularities. An experiment was conducted in which several university teachers performed a user evaluation of the tools, particularly with regard to their usefulness for reviewing past programming classes taught by another teacher. Questionnaire results showed that these tools are helpful for understanding programming processes. The tools were also found to be complementary, with different aspects being highly evaluated. We successfully presented concrete visual representations of programming processes as well as their relative strengths and weaknesses for reviewing classes; this contribution may serve as a basis for future real-time use of these tools in class.

DOI： 10.3390/su14138084

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

While positive effects of imitating other learners have been reported, the recent increases in the number of online classes have seriously limited opportunities to learn how others are learning. Providing information about others’ learning activities through dashboards could be a solution, but few studies have targeted learning activities on e-textbook systems; it remains unclear what information representations would be useful and how they would affect learning. We developed a dashboard system that enables live sharing of students’ learning activities on e-textbooks. An experiment was conducted applying the dashboard in an online class to evaluate its impact. The results of questionnaires and quizzes were analyzed along with learning activities on the e-textbook system. From the questionnaire results, the most useful feedback types were identified. Regarding the impact on learning, the study found that a higher percentage of students who used the dashboard followed the progress of the class than those who did not. The study also found that students who used the dashboard were more likely to achieve higher quiz scores than those who did not. This study is the first to reveal what specific feedback is useful and to successfully investigate the impact of its use on learning.

DOI： 10.3390/su14126946

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Theoretical Computer Science  

The authors regret that the proof for Theorem 4 in the article “On the computing powers of [Formula presented]-reductions of insertion languages” was incomplete, which was pointed out by Kaoru Fujioka, Fukuoka Women's University, Japan. The proof needs some supplementary corrections for constructing the insertion system γ and [Formula presented]. Specifically, in the proof (on page 231), 1. line 4: Correct as [Formula presented], we construct the following rules: [Formula presented] 3. after line 11: Insert the following: • If there exists a rule

DOI： 10.1016/j.tcs.2021.06.028

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Let K and L be two languages over Σ and Γ (with Γ ⊂ Σ), respectively. Then, the L-reduction of K, denoted by K%L, is defined by {u0u1⋯un∈(Σ-Γ)∗∣u0v1u1⋯vnun∈K,vi∈L(1≤i≤n)}. This is extended to language classes as follows: K%L={K%L∣K∈K,L∈L}. In this paper, we investigate the computing powers of K%L in which K ranges among various classes of INSji and min-LIN, while L is taken as DYCK and F, where INSji: the class of insertion languages of weight (j, i), min-LIN: the class of minimal linear languages, DYCK: the class of Dyck languages, and F: the class of finite languages. The obtained results include:INS11%DYCK=REINSi0%F=INSj1%F=CF (for i≥ 3 and j≥ 1)INS20%DYCK=INS20min-LIN%F1=LIN where RE, CF, LIN, F1 are classes of recursively enumerable, of context-free, of linear languages, and of singleton languages over unary alphabet, respectively. Further, we provide a very simple alternative proof for the known result min-LIN%DYCK2=RE. We also show that with a certain condition, for the class of context-sensitive languages CS, there exists no K such that K%DYCK=CS, which is in marked contrast to the characterization results mentioned above for other classes in Chomsky hierarchy. It should be remarked from the viewpoint of molecular computing theory that the notion of L-reduction is naturally motivated by a molecular biological functioning well-known as RNA splicing occurring in most eukaryotic genes.

DOI： 10.1007/s00236-022-00418-0

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Other Link： https://link.springer.com/article/10.1007/s00236-022-00418-0/fulltext.html

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Exploring the use of probabilistic latent representations to encode the students' reading characteristics

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The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior

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New Perspective on Input Feature Analysis for Early Feedback by Student Performance Prediction Considering the Future Effect

Language：Others   Publishing type：Research paper (other academic)   Publisher：ACM  

Handwritten notes are one important component of students' learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students' learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.

DOI： 10.1145/3506860.3506915

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Other Link： https://dl.acm.org/doi/pdf/10.1145/3506860.3506915

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Coding Trajectory Map: Student Programming Situations Made Visually Locatable

Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

We propose a new type of computing devices based on grammatical formulation augmented by multiset storages, called chemical reaction regular grammars (CRRGs), and investigate some formal language theoretic characterizations of CRRGs including their generative capabilities. Shortly, a CRRG is a regular grammar with multisets, while its computational capability exhibits very intriguing aspects depending on the manners of rule applications. Firstly, we show that the class of languages (denoted by CRRLλ) generated by CRRGs coincides with the class of languages accepted by chemical reaction automata (Okubo and Yokomori in Nat Comput 15(2): 215–224, 2016), whose implication is that the computing power of CRRGs is also equivalent to that of several known devices introduced from different motivations such as Petri nets (Peterson in ACM Comput Surv 9(3):223–252, 1977) and partially blind 1-way multicounter machines (Greibach in Theor Comput Sci 7:311–324, 1979). Second, a new manner of rewriting strategy is integrated into CRRGs and we show that CRRGs working in maximal-sequential manner can generate any recursively enumerable language, which is an unexpected result with a surprise. In contrast, it is also shown that regulated controls due to regular sets and matrix constraints do not enhance the computing power of CRRGs. Third, for each k≥ 1 a subclass of languages k-CRRLλ is considered, where k is the number of different symbols for multisets of CRRGs. We show that the class of languages is a full principal semi-AFL, which is obtained from a characterization result that L is in k-CRRLλ iff L= h(g- 1(Bk) ∩ R) for some homomorphisms g, h, a regular set R, where Bk is a paritally balanced language over k-symbol alphabet.

DOI： 10.1007/s00354-022-00160-8

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Other Link： https://link.springer.com/article/10.1007/s00354-022-00160-8/fulltext.html

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Publisher：Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022  

Students' performance prediction is essential for instructors to observe each student's learning behavior to discover which students have become at-risk. Early prediction helps instructors to intervene in time and provide academic support to these students. However, instructors should grasp essential behavior points to survey students' academic performance. In this study, we propose the Scaled-Dot Product Attention that can mine the relationship between the student's learning behaviors and performance to find the essential features that directly affect students' performance. In this study, we tested the early prediction performance of Scaled-Dot Product Attention with conventional algorithms. We then investigated essential lectures or features related to students' learning activities. From the result, we found that Scaled-Dot Product Attention outperformed the conventional algorithms to identify at-risk students and found the important lectures and students' actions.

DOI： 10.1109/TALE54877.2022.00059

Scopus

Publisher：CEUR Workshop Proceedings  

Student Performance Prediction is essential for learning analysis of the students' learning behavior to discovering at-risk students for the early invention to support students. This study transforms the students' reading behavior into a two-dimensional matrix input based on each lecture material's reading behavior. The matrix input will be updated by accumulating the value for each week for performance prediction week by week. The multilayer perceptron neural network is employed to receive the matrix input and give feedback as a student's criteria consist of at-risk or no-risk students. This study considers the accuracy of a model considering between on contents information and weekly information. We also investigate the switching of learning materials' order, the feature importance of the reading operation on an event stream, and the difference in reading behavior between at-risk and no-risk students. These can help the instructors for an early invention to support at-risk students.

Scopus

Publisher：CEUR Workshop Proceedings  

The emergence of digital textbook reading systems such as Bookroll, and their ability of recording reader interactions has opened the possibility of analyzing the students reading behaviors and characteristics. To date, several works have conducted compelling analyses characterizing the different types of students with the use of clustering ML models, while others have used supervised ML models to predict their academic performance. The main characteristic these models share is that internally they simplify the students' data into a latent representation to get an insight or make a prediction. Nevertheless, these representations are oversimplified, otherwise difficult to interpret. Accordingly, the present work explores the use of Variational Autoencoders to make more interpretable and complex latent representations. After a brief description of these models, we present and discuss the results of four explorative studies when using the LAK22 Data Challenge Workshop datasets. Our results show that the probabilistic latent representations generated by the proposed models preserve the student reading characteristics, allowing a better visual interpretation when using 3 dimensions. Also, they allow supervised regressive and classification models to have a more stable and less overfitted learning process, which also allows some of them to make better score predictions.

Scopus

Web of Science

Publisher：AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance  

Background subtraction networks outperform traditional hand-craft background subtraction methods. The main advantage of background subtraction networks is their ability to automatically learn background features for training scenes. When applying the trained network to new target scenes, adapting the network to the new scenes is crucial. However, few studies have focused on reusing multiple trained models for new target scenes. Considering background changes have several categories, such as illumination changes, a model trained for each background scene can work effectively for the target scene similar to the training scene. In this study, we propose a method to ensemble the module networks trained for each background scene. Experimental results show that the proposed method is significantly more accurate compared with the conventional methods in the target scene by tuning with only a few frames.

DOI： 10.1109/AVSS56176.2022.9959316

Web of Science

Scopus

Web of Science

Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s41965-021-00070-6

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020 We investigate the computing power of the following language operation %: Given two languages L1 over Σ and L2 over Γ with Γ⊂Σ, we consider the language operation L1%L2={u0u1⋯un|∃u=u0v1u1⋯vnun∈L1 and ∃vi∈L2(1≤∀i≤n)}. In this case we say that L(=L1%L2) is the L2-reduction of L1. This is extended to the language families as follows: L1%L2={L1%L2|L1∈L1,L2∈L2}. Among many works concerning Dyck-reductions, for the family of recursively enumerable languages RE, it was shown that LIN%{EQ}=RE (Jantzen & Petersen, 1994) with EQ={xnx‾n|n∈N} and that min-LIN%{D2}=RE (Hirose & Okawa, 1996, and Latteux & Turakainen, 1990), where LIN and min-LIN are the families of linear and minimal linear context-free languages, respectively. In this paper, we show that each recursively enumerable language L can be represented in the form L=K%D, for some K∈INS30 and a Dyck language D, where INS⁎0 (INS30) denotes the family of insertion languages (insertion languages where the maximum length of the string to be inserted is 3). We can refine it as INS⁎0%{D2}=RE, where D2 denotes the Dyck language over binary alphabet. For context-free languages, we show that INS30%F=CF, where F is the family of finite sets. This also derives that INS⁎0%{MIR}=CF with MIR={xx‾R|x∈{0,1}⁎}. Further, for regular languages, it is shown that each regular language R can be represented in the form R=K%F, for some K∈INS20 and a finite set F={abb‾a‾|a∈V}. We also present some results which characterize the computability and properties of L in the framework of L2-reduction of L1. It is intriguing to note that, from the DNA computing point of view, the notion of L-reduction is naturally motivated by a molecular biological functioning well-known as DNA(RNA) splicing occurring in most eukaryotic genes.

DOI： 10.1016/j.tcs.2020.11.029

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020, Springer Nature B.V. This study designed a science course following collaborative problem solving (CPS) processes, and examined the effect on students’ CPS awareness. The Limnic Eruption CPS course was implemented using a Moodle system in a tenth-grade class. Considering the complex and coordinated nature of CPS, in order to improve CPS skills, it is important to identify what are related with the development of all sub-skills of CPS. Thus this study aimed to determine potential factors that affect the use of CPS skills in students’ motivational and behavioral dimensions. Multiple data sources including learning tests, questionnaire feedback, and learning logs were collected and examined by learning analytics approach. The relationships between students’ CPS awareness with their learning motivation and learning behaviors were explored. The research findings indicated a significant positive correlation between CPS awareness and certain learning motivation factors and learning behavior factors. Considering the students’ individual differences in learning abilities, we also compared the results of high and low performance groups. As a result, low performers’ learning motivation and learning behaviors were correlated with the social domain of CPS awareness, while those of high performers were correlated with their cognitive awareness.

DOI： 10.1007/s10758-020-09436-8

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019, The Author(s). The purpose of this study was to explore the factors that might affect learning performance and collaborative problem solving (CPS) awareness in science, technology, engineering, and mathematics (STEM) education. We collected and analyzed data on important factors in STEM education, including learning strategy and learning behaviors, and examined their interrelationships with learning performance and CPS awareness, respectively. Multiple data sources, including learning tests, questionnaire feedback, and learning logs, were collected and examined following a learning analytics approach. Significant positive correlations were found for the learning behavior of using markers with learning performance and CPS awareness in group discussion, while significant negative correlations were found for some factors of STEM learning strategy and learning behaviors in pre-learning with some factors of CPS awareness. The results imply the importance of an efficient approach to using learning strategies and functional tools in STEM education.

DOI： 10.1186/s41039-019-0119-y

Language：Others   Publishing type：Research paper (other academic)  

© 2019 International Conference on Computers in Education, Proceedings.All right reserved. In this paper, we present an ontology-based visualization support system for e-book learners, which provides not only a meaningful receptive learning environment but also a meaningful discovery learning environment. Those two environments are developed to help e-book learners to effectively construct their knowledge frameworks. A series of experiments were conducted on four undergraduate classes instructed by two professors (A and B): two classes(one guided by A and the other guided by B) were assigned as control groups and studied with one e-book chapter in receptive learning environment while another two classes (one guided by A and the other guided by B) were assigned as experimental groups and studied with the same e-book chapter in discovery learning environment. For analyzing the learner behavior, K-means clustering algorithm is performed not only by considering the number of total command actions and the cumulative duration of stay on target pages as learner features, but also by considering the duration of stay on each target page (in total 15 pages) as learner features. Learners’ behavior differences in e-book system are examined and discussed.

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019 Elsevier B.V. Chemical reaction automata, computing models inspired by chemical reactions occurring in nature, have been proposed and investigated in [28]. In this paper, we introduce the notion of a chemical reaction transducer (CRT) which is defined as a chemical reaction automaton equipped with output device. We investigate the problem of decomposing CRTs into simpler component CRTs in two different forms: serial decomposition and factorization. For the serial decomposition, we give a sufficient condition for CRTs to be serially decomposable. For factorization, we show that each CRT T can be realized in the form: T(x)=g(h−1(x)∩L) for some codings g,h and a chemical reaction language L, which provides a generalization of notable Nivat's Theorem for rational transducers. This result is then elaborated in a refined form. Further, some transformational characterizations of CRTs are also discussed.

DOI： 10.1016/j.tcs.2019.01.032

Language：Others   Publishing type：Research paper (other academic)  

© 2019, Springer Nature Switzerland AG. Extended learning environments involving system to collect data for learning analytics and to support learners will be useful for all-age education. As the first steps towards to build new learning environments, we developed a system for multimodal learning analytics using eye-tracker and EEG measurement, and inclusive user interface design for elderly learners by dual-tablet system. Multimodal learning analytics system can be supportive to extract where and how learners with varied backgrounds feel difficulty in learning process. The eye-tracker can retrieve information where the learners paid attention. EEG signals will provide clues to estimate their mental states during gazes in learning. We developed simultaneous measurement system of these multimodal responses and are trying to integrate the information to explore learning problems. A dual-tablet user interface with simplified visual layers and more intuitive operations was designed aiming to reduce the physical and mental loads of elderly learners. A prototype was developed based on a cross-platform framework, which is being refined by iterative formative evaluations participated by elderlies, in order to improve the usability of the interface design. We propose a system architecture applying the multimodal learning analytics and the user-friendly design for elderly learners, which couples learning analytics “in the wild” environment and learning analytics in controlled lab environments.

DOI： 10.1007/978-3-030-22580-3_35

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018, © 2018 Taylor & Francis Group, LLC. 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.

DOI： 10.1080/10447318.2018.1543077

Language：Others   Publishing type：Research paper (other academic)  

© 2018 IEEE. This study was designed to identify correlations between students' awareness of Collaborative Problem Solving (CPS) and learning performance and learning behavior in science education. The topic of the course was Genetic Diseases which was implemented in a twelfth-grade class. To assess the effectiveness of this instructional design, and to find out potential factors that affect the using of CPS skills, multiple data sources including learning test scores, questionnaire feedback, and learning logs were analyzed. First, results indicated significant improvements in students' knowledge acquisition and awareness of Participation and Learning and Knowledge Building in CPS during the course. Furthermore, when we investigated the correlations between CPS awareness and learning performance and learning behavior, the findings indicated significant positive correlations between students' learning motivation and their awareness of CPS processes. However, there were negative correlations found between certain learning behavior factors with CPS awareness and learning motivation factors respectively.

DOI： 10.1109/TALE.2018.8615254

Language：Others   Publishing type：Research paper (other academic)  

© 2018, Springer International Publishing AG, part of Springer Nature. In Natural Computing research [18], as mathematical modeling tools of biochemical reactions, Ehrenfeucht and Rozenberg introduced a formal model called reaction systems [6] for investigating the functioning of the living cell, where two basic components (reactants and inhibitors) play a key role as a regulation mechanism in controlling interactions.

DOI： 10.1007/978-3-319-94418-0_42

Language：Others   Publishing type：Research paper (other academic)  

© Springer International Publishing AG, part of Springer Nature 2018. As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals.

DOI： 10.1007/978-3-319-91131-1_6

Language：Others   Publishing type：Research paper (scientific journal)  

© 2008-2011 IEEE. This paper is an extension of research originally reported in [1]. Here, we propose a novel method for summarizing lecture slides to enhance students' preview efficiency and understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, because the attention span of students is limited, this is not always beneficial. We surveyed 326 students regarding the preview of lecture materials, revealing a preference for summarized materials to preview. Therefore, we developed an automatic summarization method for condensing original lecture materials into a summarized set. Our proposed approach utilizes image and text processing to extract important pages from lecture materials, optimizing selection of pages in accordance with a specified preview time. We applied the proposed summarization method to a set of lecture slides. In an experiment with 372 students, we compared the effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes, even though they spent less time previewing the material.

DOI： 10.1109/TLT.2017.2682086

Language：Others   Publishing type：Research paper (other academic)  

© 2018 Association for Computing Machinery. 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.

DOI： 10.1145/3170358.3170412

Language：Others   Publishing type：Research paper (other academic)  

© 2018 CEUR-WS. All Rights Reserved. In this paper, we explore the factor for improving the performance of prediction of students’ quiz scores by using a Recurrent Neural Network. The proposed method is applied to the log data of 2693 students in 15 courses that were conducted with following the common syllabus by 10 teachers. The experimental results show that in the case where the same teacher is not included in both training and test data, the accuracy of prediction slightly lower. We also show that at the beginning of a course, it is better to construct a prediction model including various items of learning logs, however, in the latter half, it is better to update the model by using selected information only.

Language：Others   Publishing type：Research paper (scientific journal)  

© 2017, The Author(s). This research aims to investigate the relationship between self-regulated learning awareness, learning behaviors, and learning performance in ubiquitous learning environments. In order to conduct this research, psychometric data about self-regulated learning and log data, such as slide pages that learners read, marker, and annotate, was collected. The accessing activity of device types that stored the learning management system was collected and analyzed by applying path analysis and correlation analysis using data divided into high and low performers. The results indicated that the slide pages which learners read for a duration of between 240 and 299 s had positive effects on the promotion of annotation and the learning performance directly, and albeit indirectly, the enhancement of self-efficacy was affected by other self-regulated learning factors. The results of the correlation analysis indicated that self-efficacy and test anxiety are a key factor that has different effects on the number of the read slide pages in both high and low performers.

DOI： 10.1186/s41039-017-0053-9

Language：Others   Publishing type：Research paper (other academic)  

© 2017 Asia-Pacific Society for Computers in Education. All rights reserved. In this paper, we show a method to predict students' final grades using a recurrent neural network (RNN). An RNN is a variant of a neural network that handles time series data. For this purpose, the learning logs from 937 students who attended one of six courses by two teachers were collected. Nine kinds of learning logs are selected as the input of the RNN. We examine the prediction of final grades, where the training data and test data are the logs of courses conducted in 2015 and in 2016, respectively. We also show a way to identify the important learning activities for obtaining a specific final grade by observing the values of weight of the trained RNN.

Language：Others   Publishing type：Research paper (other academic)  

© 2017 Asia-Pacific Society for Computers in Education. All rights reserved. To identify “good performance,” this study analyzed the highlighting and annotating action logs of undergraduates during their e-book usage. To reveal “good performance,” the study focused on the learning behavior of high achieving students. Few highlights and annotations were observed for both rich knowledge and poor knowledge high achievers. Moreover, in the spontaneous usage of e-books outside the classroom, high and poor knowledge students did not display differences in highlights and annotations.

Language：Others   Publishing type：Research paper (other academic)  

© 2017 Asia-Pacific Society for Computers in Education. All rights reserved. E-book has been gradually getting popularity in educational contexts. Reading textbooks on computers or hand-held devices enables us to track the learning activities of students regardless of situations. In our university, several courses for first year students employs our e-book system, and we have been collecting its usage logs. From the logs, it seems that the highlighter function of the e-book reader plays an important role in learning because it is used most by the students. Though many researches studied the effectiveness of e-textbooks, only limited studies addressed how students utilize highlighters and how marking activity affects their learning. In this paper, we focus on highlighted portions of e-textbooks, and analyze how students use highlighters in their learning. We also attempt to provide recommendations to students for highlighting based on the highlighter usage in other classes.

Language：Others   Publishing type：Research paper (other academic)  

© 2017. In this paper, we present a system for visualizing learning logs of a course in progress together with predictions of learning activities of the following week and the final grades of students by state transition graphs. Data are collected from 236 students attending the course in progress and from 209 students attending the past course for prediction. From these data, the system constructs a state transition graph, where the prediction is based on the Markov property. We verify the performance of predictions by experiments in which the accuracy of prediction using the data of the course in progress and the one by 5-fold cross validation.

Language：Others   Publishing type：Research paper (other academic)  

© 2017. Students' journal writings could be useful resources for teachers to grasp their understandings and to see their own teaching objectively. However, reading a large number of journals thoroughly is not always realistic for teachers. Although various automatic analysis methods have been proposed to understand learning journals, they does not necessarily fit needs of teachers and tend to overlook minor opinions. In this paper, we propose an interactive report tool for exploring journal writings. Focusing on the efficiency of reading learning journals, it employs weekly keywords extracted from journals as entry points for journal sentences. It enables us to read journal sentences selectively. The tool also provides lists of most used adjectives from week to week, which is helpful for teachers to grasp the temporal variation of opinions through a semester. We conducted a preliminary questionnaire about the usefulness of the report tool targeting teachers of the course "Information Science" in our university. Most of them evaluated our tool positively although the number of answers were small.

Language：Others   Publishing type：Research paper (scientific journal)  

New morphic characterizations in the form of a noted Chomsky-Schützenberger theorem are established for the classes of regular languages, of context-free languages and of languages accepted by chemical reaction automata. Our results include the following: (i) Each λ-free regular language R can be expressed as R = h(Tk ∩ FR) for some 2-star language FR, an extended 2-star language Tk and a weak coding h. (ii) Each λ-free context-free language L can be expressed as L = h(Dn ∩ FL) for some 2-local language FL and a projection h. (iii) A language L is accepted by a chemical reaction automaton iff there exist a 2-local language FL and a weak coding h such that L = h(Bn ∩ FL), where Dn and Bn are a Dyck set and a partially balanced language defined over the n-letter alphabet, respectively. These characterizations improve or shed new light on the previous results.

DOI： 10.3233/FI-2017-1569

Language：Others  

DOI： 10.1007/978-3-319-55345-0_13

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：Others   Publishing type：Research paper (other academic)  

© 2017, CEUR-WS. All rights reserved. We investigated the learning behavior of undergraduates with e-book logs. E-book logs from 99 undergraduates taking an information science course were collected. First, we analyzed differences between nine high-achieving students and three low-achieving students. A log recorded before a class session in which the same e-book was used as a textbook was considered a preview log, and one recorded after a class session was considered a review log. The analysis of preview frequency indicates that the low achievers did not perform the previews, but many high achievers frequently did. The review frequency demonstrates that regardless of high and low achievements, students performed reviews. We added the logs of six relatively low achievers and analyzed more details of the preview logs of high and low achievers. The number of page flips and durations of preview logs revealed that relatively low achievers tried to perform previews, but they gave the endeavor up easily.

Language：English   Publishing type：Research paper (international conference proceedings)  

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DOI： 10.1051/ita/2013047

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DOI： 10.1016/j.tcs.2012.03.024

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Event date： 2016.5

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Venue：早稲田大学   Country：Japan  

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Venue：奈良教育大学   Country：Japan  

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Publisher：CEUR Workshop Proceedings  

Scopus

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特集「ラーニング・アナリティクス最前線」

DOI： 10.18910/70428

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：3

Proceedings of International Conference Number of peer-reviewed papers：5

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：6

Proceedings of International Conference Number of peer-reviewed papers：1

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：1

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc.