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The integration of technology and education: An innovative research on the evaluation system of college students’ programming ability
- Citation Author(s):
-
DongxuanWang
HeiBei Agricultural University - Submitted by:
- Dongxuan Wang
- Last updated:
- Thu, 11/14/2024 - 03:13
- DOI:
- 10.21227/8efa-jp06
- License:
79 Views- Categories:
- Keywords:
Abstract
This dataset comprises two main components: student performance records and competition participation details, designed to support the research outlined in the manuscript titled "The Integration of Technology and Education: An Innovative Research on the Evaluation System of College Students’ Programming Ability." The dataset includes detailed information on academic performance and competition outcomes for 307 college students. The data.xlsx file contains multiple sheets, with Sheet1 providing comprehensive academic records for each student. These records include class, name, student number, and scores across various subjects such as Advanced Mathematics, Linear Algebra, Probability Theory and Mathematical Statistics, Discrete Mathematics, Function of Complex Variables, Algorithm Analysis and Design, Data Structure, C Language Programming, Java, Compilation Principles, Python, and Scientific Computing and Data Analysis. Additionally, the sheet includes competition scores and final percentage scores for each student. Sheet2 offers insights into students' competition participation, detailing items such as competition participation status, awards won, and specific scores for events like the Lanqiao Cup and CCPC. Together, these data provide a robust foundation for evaluating and analyzing the programming abilities of college students from multiple dimensions.
The study employs a combination of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct a multidimensional evaluation system for college students' programming abilities. AHP is used to determine the relative weights of various evaluation criteria through pairwise comparisons, ensuring the system's comprehensiveness and reliability. TOPSIS is then applied to compute the comprehensive scores of each student by evaluating their performance relative to ideal and negative ideal solutions. The evaluation criteria cover key dimensions such as mathematical analysis ability, program optimization ability, basic programming design ability, practical problem-solving skills, and complex problem-solving capabilities. Each dimension includes specific secondary indicators, such as advanced mathematics and data structures. The process involves constructing a judgment matrix, performing a consistency check to ensure the reliability of the weights, and calculating the final scores. This integrated approach provides a scientific and robust evaluation tool for educational institutions, helping to optimize teaching strategies and enhance students' programming skills.
