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相关概念视频

Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Nursing Process for Patient and Caregiver Teaching III: Evaluation and Documentation01:20

Nursing Process for Patient and Caregiver Teaching III: Evaluation and Documentation

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Evaluation of the teaching process enables the nurse to determine if the patient's learning needs were met and if training was effective. If the expected outcomes are not met, the care plan is revised, and additional education or reinforcement is provided. Nurses can ask questions after the session or obtain feedback to assess the patient's understanding of the topic.
Nurses can use several methods to evaluate patient outcomes. For example, oral questions can assess cognitive learning,...
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Nursing Evaluation01:15

Nursing Evaluation

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The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
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Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

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Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

Updated: Jan 17, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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提高高等教育机构的课程评估流程:模块化系统方法.

İlker Kocaoğlu1, Erinç Karataş2

  • 1Management Information Systems, Baskent University, Ankara, Turkey.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

本研究引入了一个模块化系统,通过检测数字和文本反之间的不一致性来改进课程和教师评估 (CIE). 该系统通过可靠的数据洞察提高了教育质量评估.

科学领域:

  • 教育技术的教育技术
  • 教育中的人工智能
  • 高等教育中的数据科学

背景情况:

  • 传统的课程和教师评估 (CIE) 面临着结构化响应和开放式反之间的不一致数据的挑战.
  • 现有的方法往往无法将数字分数与文本评论相协调,导致不可靠的洞察力和增加的行政负担.
  • 提高CIE数据的可靠性对于高等教育的有效决策至关重要.

研究的目的:

  • 设计和评估一种新的模块化系统,以提高课程和教师评估的准确性和可靠性.
  • 通过整合情绪分析和不一致性检测来解决CIE数据中的不一致性.
  • 为高等教育机构提供可扩展和适应的数据驱动决策解决方案.

主要方法:

  • 利用设计科学研究方法 (DSRM) 开发了一个五模块系统架构.
  • 采用机器学习算法,包括GPT-4 Turbo Preview,分析了13651个匿名的土耳其CIE记录.
  • 对比情绪分析的结果是开放式反与结构化响应以识别数据不一致.

主要成果:

  • GPT-4 Turbo Preview 模型在情绪分析和不一致性检测方面表现出卓越的性能.
  • 一个原型系统在CIE数据的一个子集中发现了37%的不一致率.
关键词:
DSRM DSRM 是一个很好的方法.高等教育 高等教育机器学习是机器学习.情绪分析是一种情绪分析.

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  • 排除不一致的数据产生了可靠的报告,对课程和教师表现有可操作的见解.

    • 结论:

    • 拟议的模块化系统有效地提高了课程和教师评估的准确性和可靠性.
    • 该系统为寻求改善教育质量评估的高等教育机构提供了可扩展和适应的解决方案.
    • 集成先进的机器学习技术代表了利用技术提高教育水平的重大进步.