Learning analytics platform in higher education in Japan

Flanagan, Brendan; Ogata, Hiroaki

doi:10.34105/j.kmel.2018.10.029

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…The recommender system was implemented as an application module in the Learning Evidence Analytics Framework (LEAF) (Flanagan & Ogata, 2018), which includes the learning management system Moodle to manage students and course information, the ebook reading application BookRoll to view and answer math quizzes, and the recommender system to view recommendations.…”

Section: Methodsmentioning

confidence: 99%

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

Dai,

Takami,

Flanagan

et al. 2023

RPTEL

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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.

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Section: Methodsmentioning

confidence: 99%

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

Dai,

Takami,

Flanagan

et al. 2023

RPTEL

View full text Add to dashboard Cite

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“…As part of the efforts in the domain, several interesting AI-based tools and applications have been introduced to facilitate educators in different ways. Moreover, several international standards, such as Caliper, xAPI, NGDLE (Next Generation Digital Learning Environment), are proposed to provide guidelines for data collection, storage, and analytics in for these applications [25]. In the next sub-sections, we provide an overview of existing literature in these applications as well as the tools and platforms developed as part of these efforts.…”

Section: Ai Applications In Educationmentioning

confidence: 99%

Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Ahmad,

Iqbal,

El-Hassan

et al. 2024

IEEE Trans. Learning Technol.

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As Education constitutes an essential development standard for individuals and societies, researchers have been exploring the use of Artificial Intelligence (AI) in this domain and have embedded the technology within it through a myriad of applications. In order to provide a detailed overview of the efforts, this article pays particular attention to these developments by highlighting key application areas of data-driven AI in Education; it also analyzes existing tools, research trends, as well as limitations of the role data-driven AI can play in Education. In particular, the article reviews various applications of AI in Education including student grading and assessments, student retention and drop-out predictions, sentiment analysis, intelligent tutoring, classroom monitoring and recommender systems. The article also provides detailed bibliometric analysis to highlight the salient research trends in AI in Education over seven years (2014)(2015)(2016)(2017)(2018)(2019)(2020) and further provides detailed description of the tools and platforms developed as the outcome of research and development

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“…Since April 2019, we have been offering an LMS-integrated LA platform called LEAF (Learning Evidence Analytics Framework) to a junior high school in Japan. LEAF offers three online learning tools to teachers -Moodle, BookRoll, and LAViEW (Flanagan & Ogata, 2018).…”

Section: Leaf Platformmentioning

confidence: 99%

Learning analytics for student homework activities during a long break: Evidence from K-12 education in Japan

Kuromiya,

Majumdar,

Horikoshi

et al. 2024

RPTEL

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Learning Analytics (LA) is an emergent field that aims to better understand students and provide intelligence to learners, teachers, and administrators using learning log data. Although the use of technology in class is increasing in the K-12 sector and tertiary education, cases of effective implementation of LA in secondary schools have rarely been reported. This study offers an example of LA implemented in a junior high Math class during long vacations in Japan. This paper comprises two studies: first, we analyzed 121 students’ answer logs and their exam performance after vacation by the K-means clustering method. We found that students’ progress patterns were categorized into four types of engagement—early, late, high, and low—and the early and high-engagement groups obtained significantly higher scores than the low-engagement group. In the second study, we implemented a real-time dashboard that visualizes students’ progress patterns and gives students insights about their progress during the vacation period. We found that the dashboard significantly increased students’ interactions with the assignment, and the questionnaire survey determined that the LA dashboard motivated students to learn during the long vacation period. Considering the previous studies of LA, we estimate that LA-based interventions enhance students’ self-regulation skills, which is crucial for learning during long vacation periods. Our study offers a novel approach to implementing LA in K-12 education.

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Learning analytics platform in higher education in Japan

Cited by 18 publications

References 23 publications

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

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

Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Learning analytics for student homework activities during a long break: Evidence from K-12 education in Japan

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