Human-Machine Course Evaluation With an ALBERT-Based Position Embedding Feature Enhancement Network
Year: 2025
Type: article
Abstract: As online learning grows, effective course evaluation helps educators improve teaching and guides students in course selection. However, current sentiment analysis models for course reviews face two key issues: (1) they fail to adequately capture positional information in text, limiting their ability to understand semantic and contextual relationships, and (2) they overlook the correlation between review content and ratings. To address these, we propose the ALBERT-based position embedding feature enhancement network (ALBERT-PEFE). Our model combines local and global positional information through a position encoding module and designs a feature enhancement module to adjust the weights of text and rating features, capturing their correlations and modeling the alignment between review content and ratings. Experiments on a MOOC dataset show that ALBERT-PEFE outperforms baseline models. Additionally, we designed a four-level course evaluation framework using a human-machine collaborative approach, offering actionable insights for educators and reliable references for students.
DOI: 10.4018/ijkm.371413
Author: Xingwei Chen
Institution: Jinhua University of Vocational Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4408427835