Conservation Against Conservation: Contesting Ways of Understanding Forests in Southern Myanmar

M.A.M.A. Thesis. University of Hawaiʻi at Mānoa 201

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

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

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

Beyond recommendation acceptance: explanation’s learning effects in a math recommender system

Recommender systems can provide personalized advice on learning for individual students. Providing explanations of those recommendations are expected to increase the transparency and persuasiveness of the system, thus improve students’ adoption of the recommendation. Little research has explored the explanations’ practical effects on learning performance except for the acceptance of recommended learning activities. The recommendation explanations can improve the learning performance if the explanations are designed to contribute to relevant learning skills. This study conducted a comparative experiment (N = 276) in high school classrooms, aiming to investigate whether the use of an explainable math recommender system improves students’ learning performance. We found that the presence of the explanations had positive effects on students’ learning improvement and perceptions of the systems, but not the number of solved quizzes during the learning task. These results imply the possibility that the recommendation explanations may affect students’ meta-cognitive skills and their perceptions, which further contribute to students’ learning improvement. When separating the students based on their prior math abilities, we found a significant correlation between the number of viewed recommendations and the final learning improvement for the students with lower math abilities. This indicates that the students with lower math abilities may benefit from reading their learning progress indicated in the explanations. For students with higher math abilities, their learning improvement was more related to the behavior to select and solve recommended quizzes, which indicates a necessity of more sophisticated and interactive recommender system

Learning analytics platform in higher education in Japan

In recent years, learning analytics has become a hot topic with many institutes deploying learning management systems and learning analytics tools. In this paper, we introduce learning analytics platforms that have been established in two top national Japanese universities. These initiatives are part of a broader research project into creating wide-reaching learning analytics frameworks. The aim of the project is to support education and learning through research into educational big data accumulated on these platforms. We also discuss the future direction of our research into learning analytics platforms. This includes introducing a model in which learning analytics tools and the results of research can be shared between different education institutes

LAView: Learning Analytics Dashboard Towards Evidence-based Education

The 9th International Learning Analytics and Knowledge (LAK) Conference : March 4-8, 2019, Tempe, Arizona, USALearning analytics dashboards (LAD) have supported prior finds that visualizing learning behavior helps students to reflect on their learning. We developed LAViEW, a LAD that can be easily integrated with different learning environments through LTI. In this paper, we focus on the context of eBook-based learning and present an overview of the indicators of engagement that LAView visualizes. Its integrated email widget enables the teacher to directly send personalized feedbacks to selected cohorts of students, clustered by their engagement scores. These interventions and dashboard interactions are further tracked to extract evidence of learning

An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz

Part of the Lecture Notes in Computer Science book series (LNCS, volume 13356)Little research has addressed how systems can use the learning process of self-explanation to provide scaffolding or feedback. Here, we propose a model automatically generating sample self-explanations with knowledge components required to solve a math quiz. The proposed model contains three steps: vectorization, clustering, and extraction. In an experiment using 1434 self-explanation answers from 25 quizzes, we found 72% of the quizzes generated sample answers with all necessary knowledge components. The similarity between human-created and machine-generated sentences was 0.719, with a significant correlation of R = 0.48 for the best performing generation model by BERTScore. These results suggest that our model can generate sample answers with the necessary key knowledge components and be further improved by using the BERTScore