A  Study on Taxonomy of Innovations

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners’ dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance.

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“…e support vector machine is based on the dimensional theory and the principle of minimum structural risk [20][21][22].…”

Section: Principles and Improvement Methods Of Support Vectormentioning

confidence: 99%

Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis

Yin

Tsai

2021

Mathematical Problems in Engineering

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“…They are capable of involving students in deep learning while serving as a meaningful way for both students and faculty to engage in outcomes-based assessment. E-portfolios have been shown to be a valid way to document student progress, encourage greater student involvement in the learning process, showcase work samples, and provide a method of learning outcomes assessment and curriculum evaluation [1]. E-portfolios have become an important e-learning tool because of their potential to support more student-centered learning, reflective practices and personalized forms of learning.…”

Section: Introductionmentioning

confidence: 99%

E-Portfolio: A Potential E-Learning Tool to Support Student-Centered Learning, Reflective Learning and Outcome-Based Assessment Perspective

C. Bangalan,

B. Hipona

2021

GMIT

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In search of understanding about knowledge and learning on innovation performance

Pigola

Costa

2022

Scientometrics

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A Study on Taxonomy of Innovations

Cited by 4 publications

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Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis

Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis

E-Portfolio: A Potential E-Learning Tool to Support Student-Centered Learning, Reflective Learning and Outcome-Based Assessment Perspective

In search of understanding about knowledge and learning on innovation performance

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