Event Graph Construction Based on Disciplinary Procedural Knowledge: Concept Model and Application

Xiao, Yao; Zhan, Zehui; Yuan, Man

doi:10.1145/3535756.3535770

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is necessary for teachers to encourage learners to actively participate in discussions during the teaching process. Teachers can take the initiative to post discussion information or actively ask questions related to the course content after class to guide learners to actively share their learning experiences (Xiao et al , 2022). In addition, the text-based asynchronous communication mode provides learners with a more autonomous learning experience (Koç, 2017), while some of the more shy learners can also engage more in the asynchronous interaction mode, which also influences the learners’ learning to some extent.…”

Section: Discussionmentioning

confidence: 99%

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Tong

Zhan

2023

ITSE

Self Cite

View full text Add to dashboard Cite

Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

show abstract

Section: Discussionmentioning

confidence: 99%