Construction and Design of an Academic Ability Evaluation System for Vocational College Students Fused K-means Algorithm

The existing academic ability evaluation standards for vocational college students overly focus on commonalities while neglecting individual differences among students, and place too much emphasis on teaching outcomes while ignoring the teaching process. To effectively evaluate the individual differ...

Full description

Saved in:
Bibliographic Details
Published in:2024 7th International Conference on Computer Information Science and Application Technology (CISAT) pp. 701 - 705
Main Author: Bu, Xing
Format: Conference Proceeding
Language:English
Published: IEEE 12.07.2024
Subjects:
Academic ability evaluation
Big Data
Computational modeling
Education
Exploratory factor analysis
Indexes
Indicator system
Information science
K-means clustering
Reliability
Simulation
Technological innovation
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The existing academic ability evaluation standards for vocational college students overly focus on commonalities while neglecting individual differences among students, and place too much emphasis on teaching outcomes while ignoring the teaching process. To effectively evaluate the individual differences and academic abilities exhibited by vocational college students during online learning, this paper constructs an academic ability evaluation index system, uses exploratory factor analysis to condense the indicator items, and further divides and labels learners through the k-means algorithm, creating learner portraits. Taking the learning situation of practical training platform courses for students majoring in big data and accounting at a certain college as the experimental subject, the simulation results show that the constructed academic ability evaluation system includes 5 dimensions and 16 specific measurement indicators, demonstrating good reliability and validity. After clustering, students of different categories show significant differences in evaluations across the 5 dimensions, and groups with lower multidimensional evaluations can be effectively identified. Subsequently, precise teaching intervention strategies can be designed for the target groups to improve teaching quality.
DOI:10.1109/CISAT62382.2024.10695310