A mixture transition distribution modeling for higher-order circular Markov processes

The stationary higher-order Markov process for circular data is considered. We employ the mixture transition distribution (MTD) model to express the transition density of the process on the circle. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models. The structures of the circular autocorrelation function together with the circular partial autocorrelation function are found to be similar to those of the autocorrelation and partial autocorrelation functions of the real-valued autoregressive process when the underlying binding density has zero sine moments. The validity of the model is assessed by applying it to some Monte Carlo simulations and real directional data

Extensive Reading Using an E-Book System and Online Forum

This paper presents an extensive reading project conducted on an e-book system.We use picture books and comic books as reading materials, and provide an online forum where students can share and discuss their impressions of these. As initial results of the project, we show students’ reading patterns, the influence of the online forum on reading amounts, and the influence of reading amounts on performance. The results indicate that the forum may stimulate students and encourage them to continue doing extensive reading.We also observed moderate correlations between the reading amounts and exam scores

An Evaluation of a Meaningful Discovery Learning Support System for Supporting E-book User in Pair Learning

In this paper, an experiment was conducted to study the learning performance when learning new knowledge in groups with an e-book system and a meaningful discovery learning support environment. The participants studied target new knowledge with an e-book in pairs; at first, all the knowledge points that appear in the e-book were displayed and learners in each pair were encouraged to actively create relations between the knowledge concepts together; after completing the task, they can compare their learner-generated relations with expert-generated relations. The learning perception of one hundred and forty-three participants are analyzed and discussed

Using Participatory Simulation Support Learning Algorithms

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 PLASPS (PDA-based Learning Algorithm System Using Participatory Simulation). 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. This system is like ‘scaffolding’. Scaffolding is a great technique that can help the students to master understanding the sorting algorithm. At the beginning, this system assists the students by supporting some instructions, and later the fading process is starting where the students have to practice independently. There are two parts in this system, one is the system-driven, which uses scaffolding technique, and the other is the learner-driven, which allows the student to work independently. This system was developed and evaluated. In this paper, we describe how the system uses participatory simulation environment for sorting algorithm learning, how we use the scaffolding technique to develop this system. We also describe the implementation of the PLASPS, the evaluation of the system and the plan of the future work.Dept. of information science and Intelligent Systems, University of Tokushima, Japa

Learning log-based automatic group formation: system design and classroom implementation study

Collaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems

Fostering Evidence-Based Education with Learning Analytics: Capturing Teaching-Learning Cases from Log Data

Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners’ workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts – one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners

La technologie du livre électronique pour faciliter l'enseignement universitaire pendant la COVID-19 : Expérience Japonaise

UNESCO reported that 90% of students are affected in some way by COVID-19 pandemic. Like many countries, Japan too imposed emergency remote teaching and learning at both school and university level. In this study, we focus on a national university in Japan, and investigate how teaching and learning were facilitated during this pandemic period using an ebook platform, BookRoll, which was linked as an external tool to the university’s learning management system. Such an endeavor also reinforced the Japanese national thrust regarding explorations of e-book-based technologies and using Artificial Intelligence in education. Teachers could upload reading materials for instance their course notes and associate an audio of their lecture. While students who registered in their course accessed the learning materials, the system collected their interaction logs in a learning record store. Across the spring semesters from April - July 2020, BookRoll system collected nearly 1.5 million reading interaction logs from more than 6300 students across 243 courses in 6 domains. The analysis highlighted that during emergency remote teaching and learning BookRoll maintained a weekly average traffic above 1, 900 learners creating more than 78, 000 reading logs and teachers perceived it as useful for orchestrating their course.L'UNESCO a signalé en mars 2020 que 84, 5 % du total des étudiant·e·s inscrits sont affectés d'une manière ou d'une autre par la pandémie de COVID-19, avec plus de 166 fermetures d'écoles à la grandeur de ces pays (UNESCO, 2020). Le Japon a lui aussi imposé un enseignement et un apprentissage à distance d'urgence, tant au niveau des écoles que des universités. Dans cette étude, nous nous concentrons sur une université nationale du Japon, et nous examinons comment l'enseignement et l'apprentissage ont été facilités pendant cette période de pandémie en utilisant une plateforme de livres électroniques, soit la plateforme BookRoll. En tant qu'outil externe, BookRoll a été relié au système de gestion de l'apprentissage de l'université. Cette initiative a également renforcé la volonté nationale japonaise d'explorer les technologies basées sur les livres électroniques et d'utiliser l'intelligence artificielle (IA) dans l'enseignement. Les enseignant·e·s pouvaient télécharger du matériel de lecture, par exemple leurs notes de cours, et y associer un enregistrement audio de leur prestation. Pendant que les étudiant·e·s inscrits à leur cours accédaient au matériel d'apprentissage, le système collectait leurs interactions dans un registre d'apprentissage. Au cours des semestres du printemps, d'avril à juillet 2020, le système BookRoll a recueilli près de 1, 5 million d’interactions concernant les lectures de plus de 6 300 étudiant·e·s dans 243 cours de 6 domaines, avec plus de 1 900 apprenant·e·s qui avaient créé plus de 78 000 entrées de journal, en mode lecture, par semaine. Bien que ce soit les cours de sciences et d'ingénierie qui ont principalement utilisé la plateforme, les cours de droit et d'études linguistiques l’ont utilisée pour y déposer des enregistrements audio associés à des documents à lire. L'analyse des interactions des étudiant·e·s avec le contenu a révélé que les actions d'apprentissage actif, telles que l'utilisation d'annotations sur le texte, étaient plus fréquentes dans les cours de sciences humaines. Enfin, des recommandations ont été formulées sur la base de l'analyse et de la perception des enseignant·e·s sur l'enseignement et l'apprentissage à distance d'urgence en utilisant le système BookRoll pour orchestrer leur cours

Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis

Digitized learning materials are a core part of modern education, and analysis of the use 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-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement 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 strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system

Fine Grain Synthetic Educational Data: Challenges and Limitations of Collaborative Learning Analytics

While data privacy is a key aspect of Learning Analytics, it often creates difficulty when promoting research into underexplored contexts as it limits data sharing. To overcome this problem, the generation of synthetic data has been proposed and discussed within the LA community. However, there has been little work that has explored the use of synthetic data in real-world situations. This research examines the effectiveness of using synthetic data for training academic performance prediction models, and the challenges and limitations of using the proposed data sharing method. To evaluate the effectiveness of the method, we generate synthetic data from a private dataset, and distribute it to the participants of a data challenge to train prediction models. Participants submitted their models as docker containers for evaluation and ranking on holdout synthetic data. A post-hoc analysis was conducted on the top 10 participant’s models by comparing the evaluation of their performance on synthetic and private validation datasets. Several models trained on synthetic data were found to perform significantly poorer when applied to the non-synthetic private dataset. The main contribution of this research is to understand the challenges and limitations of applying predictive models trained on synthetic data in real-world situations. Due to these challenges, the paper recommends model designs that can inform future successful adoption of synthetic data in real-world educational data systems

Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning

Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty. However, most CAT models are designed for one-time evaluation rather than improving learning through formative assessment. Since students cannot remember everything, encouraging them to repeatedly evaluate their knowledge state and identify their weaknesses is critical when developing an adaptive formative assessment system in real educational contexts. This study aims to achieve this goal by proposing an adaptive formative assessment system based on CAT and the learning memory cycle to enable the repeated evaluation of students' knowledge. The CAT model measures student knowledge and item difficulty, and the learning memory cycle component of the system accounts for students’ retention of information learned from each item. The proposed system was compared with an adaptive assessment system based on CAT only and a traditional nonadaptive assessment system. A 7-week experiment was conducted among students in a university programming course. The experimental results indicated that the students who used the proposed assessment system outperformed the students who used the other two systems in terms of learning performance and engagement in practice tests and reading materials. The present study provides insights for researchers who wish to develop formative assessment systems that can adaptively generate practice tests