Predictive Modeling of Academic Performance of Online Learners Based on Data Mining

Cheng, Zhi

doi:10.1007/978-981-33-4572-0_27

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Their personal background information and online learning behavior data were collected as the study sample, and private information such as learners' names were anonymized and normalized for characteristics such as the length of online learning and the average time to submit assignments from the due date that differed too much in value. Referring to the study of Chen Zijian et al the learners' learning performance was divided into risk categories of failure with a cut-off of 70 [17] , less than or equal to 70 was classified as risky and greater than 70 was classified as no risk.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Design of Personalized Early Warning Feedback for Distance Learning from the Perspective of Participatory Design

2023

AJCIS

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Based on a participatory design perspective, the results of data analysis and constructed predictive models are applied to the design process of early warning feedback, which contributes to the selection of representative participants and early warning feedback elements. The participatory design process in this paper includes three stages of comprehending, creating and interacting, and includes participatory design methods such as teaching activity maps, persona profile, learner insight analysis maps and learning journey maps. The study selected 15 participants from different backgrounds to design and implement the learning early warning dashboard, and 38 learners were organized to evaluate the usability of the learning early warning dashboard in four dimensions: usefulness, cognitive load, user satisfaction, and self-direction, and the results indicated that 91.1% of the learners thought that the learning early warning dashboard would allow them to quickly and easily grasp their learning and would motivate them to learn and improve in a timely manner based on the learning strategies provided.

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Section: Data Collection and Pre-processingmentioning

confidence: 99%

Design of Personalized Early Warning Feedback for Distance Learning from the Perspective of Participatory Design

2023

AJCIS

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“…The issues of learning behavior diagnosis and intelligent monitoring of the learning process are among the research directions of learning analytics and data mining, and it is crucial to analyze the process of learning occurrence [10]. Currently, learning analytics is mainly applied in online or blended learning environments to predict or warn students' academic performance with secondary school and university students as research objects [12][13][14][15], to explore students' online learning motivation or self-regulation characteristics [16][17][18]. Overall, with the development of online learning spaces, empirical studies of learning analysis based on actual courses and teaching using technologies such as intelligent recording systems, IoT sensing technologies, and online learning and management platforms have gradually increased, but there is insufficient analysis of individual learning characteristics and behavioral analysis [19], and there is still a lack of micro-level teaching and learning that can be facilitated.…”

Section: Literature Review 21 Study Of Learning Analytics Applied To ...mentioning

confidence: 99%

Game-Based Formative Assessment of Analogical Reasoning in Preschool Children: Support from the Internet of Things Technology

Guo

et al. 2022

Sustainability

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Emerging information technology such as Internet of Things (IoT) has been continuously applied and deepened in the field of education, and the learning analytics technology based on children’s games is gradually moving toward practical application research, but there are few empirical studies on the micro level of emerging information technology and learning analytics methods in the evaluation of young children’s learning process and learning effects. As the research content, the study examines preschool children’s analogical reasoning abilities, reflecting their thinking levels and processing abilities. Using a decision tree model in learning analytics, the process data and result data of children’s analogical reasoning games based on Internet of Things technology are analyzed, and the classification model of preschool children’s analogical reasoning is constructed. The study found that the learning analysis of analogical reasoning based on games mediated by IoT technology is feasible and effective.

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Predictive Modeling of Academic Performance of Online Learners Based on Data Mining

Cited by 2 publications

References 8 publications

Design of Personalized Early Warning Feedback for Distance Learning from the Perspective of Participatory Design

Design of Personalized Early Warning Feedback for Distance Learning from the Perspective of Participatory Design

Game-Based Formative Assessment of Analogical Reasoning in Preschool Children: Support from the Internet of Things Technology

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