Online Learning Behavior Feature Mining Method Based on Decision Tree

Shao, Juxin; Gao, Qian; Wang, Hui

doi:10.4018/jcit.295244

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Recently, relevant researches in the field of machine learning suggest decision trees can be used as effective predictive models in the predictive analysis of placement of students [6], students' study path selection [7], detecting undesirable students' behaviors and evaluating students' performance in an E-learning environment [8] [9]. Likewise, as an effective data-mining tool, decision trees can also be used in online learning behavior feature mining [10] and the computation of students' academic performance [11]. Noticeably, scholars have been using decision tree algorithms and stratification analyses in the discovery of learners' need for teaching videos [12], even in the prediction of learners' mental state [13], introducing individual variables such as learner's prior knowledge, background, e-learning experience and opening new cognitive and emotional dimensions in the interdisciplinary field of machine learning and smart teaching such as learners' inner needs and motives that affect the working of personality system in general.…”

Section: ．Introductionmentioning

confidence: 99%

An Empirical Approach to the Utilization of Affective Decision Tree Models in Smart Teaching

Li¹

2023

Modern Management Based on Big Data IV

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In the upcoming age of AI, smart education reshapes not only the macro modality of society and education but also the micro modality of knowledge and learning. The top priority of smart education is the timely learning disability diagnosis and feedback, the assessment and improvement of students’ mental quality, in which affective data is the crucial index. Putting in perspective the working of positive and negative affects in smart teaching, this research proposes the affective decision tree model drawing on the affective data collected from the PANAS test. Based on the C5.0 algorithm, classification rules are extracted from the calculation of the information gain ratio of affective variables and the construction of decision tree is realized through SPSS 26.0. The research findings indicate that 1) negative affects took precedence over positive affects in the formulation of participants’ smart learning strategy; 2) the architecture of the affective decision tree constructed via learners’ affective judgments and ratings represents the affective filtering process which optimizes teachers’ selection and organization of smart teaching and learning activities. Accordingly, the affective decision tree model could be used as the efficient prediction model and affective profiling model and teaching support system in smart teaching, prompting the affective and cognitive connectivity between teachers and students.

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Section: ．Introductionmentioning

confidence: 99%

An Empirical Approach to the Utilization of Affective Decision Tree Models in Smart Teaching

Li¹

2023

Modern Management Based on Big Data IV

View full text Add to dashboard Cite

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The Effect of Cloud Tracking-Based Integrated Supply Chain Learning Devices on Critical Thinking Skills, Learning Behavior, Student Career Development with Variable Moderation Learning Media

Dharma,

Churiyah,

Suputra

et al. 2024

RGSA

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Purpose: In this context, integrated supply chain learning (ISCL) is defined as a learning approach that integrates learning resources from various parties. This research aims to examine the effect of using ISCL on students' critical thinking skills, students' learning behavior, students' career development, and this research will also consider moderating variables related to learning media. Theoretical framework: In the current era of globalization and technological progress, the need for learners equipped with relevant skills to face the challenges of the 21st century is increasingly urgent. Critical thinking skills, adaptive learning behavior, and career development are the three main aspects that are the focus in preparing students to be successful in an ever-changing world. Method/design/approach: This research methodology adopts a quantitative approach using surveys as a data collection instrument. Results and conclusion: The results show that learning media has a significant role in mediating the relationship between technological devices and students' critical thinking skills, learning behavior, and career development. Research implications: The implication of this research is the importance of integrating learning media in the education curriculum to prepare students with skills relevant to industry needs. Originality/value: This research has a broad and positive impact in improving the education system, strengthening links between the world of education and industry, and providing better opportunities for students to achieve success in the world of work.

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Online Learning Behavior Feature Mining Method Based on Decision Tree

Cited by 2 publications

References 27 publications

An Empirical Approach to the Utilization of Affective Decision Tree Models in Smart Teaching

An Empirical Approach to the Utilization of Affective Decision Tree Models in Smart Teaching

The Effect of Cloud Tracking-Based Integrated Supply Chain Learning Devices on Critical Thinking Skills, Learning Behavior, Student Career Development with Variable Moderation Learning Media

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